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!default;\n$enable-responsive-font-sizes: true !default;\n// Set different default as Bootstrap's choice is a very flashy blue\n$primary: #074 !default;\n\n// FontAwesome\n$fa-font-path: '/assets/webfonts' !default;\n\n\n// Import Sources\n\n// Style Framework: Bootstrap\n@import 'bootstrap/bootstrap';\n// Icons: FontAwesome\n@import 'font-awesome/fontawesome';\n@import 'font-awesome/regular';\n@import 'font-awesome/solid';\n// Map Display Framework: Leaflet\n@import 'leaflet/leaflet';\n@import 'leaflet/leaflet-easybutton';\n@import 'leaflet/leaflet-locatecontrol';\n\n\n// Adapt Bootstrap Elements\n\n// Alert\n.alert {\n //$alert-border-radius\n border-width: 0px;\n border-left-width: $alert-border-width;\n}\n\n@each $color, $value in $theme-colors {\n .border-soft-#{$color} {\n border-color: theme-color-level($color, $alert-border-level);\n border-left-width: $alert-border-width;\n border-left-style: solid;\n }\n .bg-soft-#{$color} {\n @include gradient-bg(theme-color-level($color, $alert-bg-level));\n }\n}\n\n// Button group for lists\nul.btn-group {\n > li:not(:first-child) > .btn {\n margin-left: -$btn-border-width;\n @include border-left-radius(0);\n }\n\n > li:not(:last-child) > .btn {\n @include border-right-radius(0);\n }\n}\n\n// Extend Bootstrap Elements\n.display-5 {\n font-size: 3rem;\n font-weight: $display4-weight;\n line-height: $display-line-height;\n}\n.font-normal {\n .badge {\n font-size: .85em;\n }\n}\n.overflow-y-hidden {\n overflow-y: hidden;\n}\n\n// Custom Styling\n\n// General\n\n// Keep images in container\nmain.container img {\n max-width: 100%;\n height: auto;\n}\n\n// No bottom margin on info-bar\n.alert > p:last-of-type {\n margin-bottom: 0;\n}\n\n// Live icon\n.live-button > object {\n width: 0.85em;\n height: 0.85em;\n color: inherit;\n}\n\n// Live button font size in navigation bar\n.nav-link .live-button {\n font-size: 1em;\n}\n\n// Program\n\n// Sticky header and first column\n.table {\n // Avoid sticky cells without border\n border-collapse: separate;\n\n // Correct border-width caused by border-collapse\n border-spacing: 0px;\n tbody tr:first-of-type > * {\n border-top-width: 0px;\n }\n\n // Sticky horizontal positioning\n th {\n position: sticky;\n background-color: $body-bg;\n }\n th:first-of-type {\n left: 0px;\n z-index: 1;\n }\n\n // Hide shadow of table cells next to sticky header\n thead tr > th:last-of-type {\n box-shadow: 10px 0px 0px 0px rgba(255,255,255,1);\n }\n}\n\n// Column Width\n.table {\n // First row defines width for all columns\n table-layout: fixed;\n\n // Columns define table width\n width: auto;\n\n // Column widths\n th, td {\n // Total available container width is 1110px\n\n // Width of columns with content (default)\n width: 245px;\n min-width: 245px;\n\n // Spacing between columns\n &.col-space {\n width: 15px;\n min-width: 15px;\n }\n // Width of first column containing timestamps\n &.col-title {\n width: 70px;\n min-width: 70px;\n }\n }\n}\n\n// Location\n.nav-pills .nav-link.active {\n background-color: theme-color-level('primary', $alert-bg-level);\n color: var(--dark);\n}\n#map {\n height: 50vh;\n min-height: 400px;\n width: 100%;\n}\n\n// Program per Room\n.list-program {\n border: 0 none white;\n\n border-collapse: separate;\n border-spacing: 0 0.5em;\n\n tr {\n td:first-child {\n vertical-align: top;\n }\n\n td:last-child {\n width: 100%;\n }\n }\n}\n\n// Speaker\n.speakergroup {\n page-break-inside: avoid;\n break-inside: avoid-column;\n}\n@include media-breakpoint-up(sm) {\n .speakerlist {\n column-count: 1;\n }\n}\n@include media-breakpoint-up(md) {\n .speakerlist {\n column-count: 2;\n }\n}\n@include media-breakpoint-up(lg) {\n .speakerlist {\n column-count: 3;\n }\n}\n\n\n// Print\n@media print {\n // remove underline for links\n a {\n text-decoration: none !important;\n }\n\n // overwrite bootstrap default white background\n // works only with Webkit (Chrome, Safari) and Firefox\n @each $color, $value in $theme-colors {\n .table td.alert-#{$color} {\n color-adjust: exact;\n background-color: theme-color-level($color, $alert-bg-level) !important;\n\n color: black;\n }\n }\n .badge {\n color-adjust: exact;\n\n border: 0 none white;\n text-decoration: none !important;\n }\n\n // Remove sticky position from table to show borders\n .table th {\n position: inherit;\n }\n\n // Remove live buttons\n .live-show {\n display: none;\n }\n .live-hide {\n display: inline-block !important;\n }\n}\n", + "/*!\n * Bootstrap v4.5.3 (https://getbootstrap.com/)\n * Copyright 2011-2020 The Bootstrap Authors\n * Copyright 2011-2020 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n */\n\n@import \"functions\";\n@import \"variables\";\n@import \"mixins\";\n@import \"root\";\n@import \"reboot\";\n@import \"type\";\n@import \"images\";\n@import \"code\";\n@import \"grid\";\n@import \"tables\";\n@import \"forms\";\n@import \"buttons\";\n@import \"transitions\";\n@import \"dropdown\";\n@import \"button-group\";\n@import \"input-group\";\n@import \"custom-forms\";\n@import \"nav\";\n@import \"navbar\";\n@import \"card\";\n@import \"breadcrumb\";\n@import \"pagination\";\n@import \"badge\";\n@import \"jumbotron\";\n@import \"alert\";\n@import \"progress\";\n@import \"media\";\n@import \"list-group\";\n@import \"close\";\n@import \"toasts\";\n@import \"modal\";\n@import \"tooltip\";\n@import \"popover\";\n@import \"carousel\";\n@import \"spinners\";\n@import \"utilities\";\n@import \"print\";\n", + "// Bootstrap functions\n//\n// Utility mixins and functions for evaluating source code across our variables, maps, and mixins.\n\n// Ascending\n// Used to evaluate Sass maps like our grid breakpoints.\n@mixin _assert-ascending($map, $map-name) {\n $prev-key: null;\n $prev-num: null;\n @each $key, $num in $map {\n @if $prev-num == null or unit($num) == \"%\" or unit($prev-num) == \"%\" {\n // Do nothing\n } @else if not comparable($prev-num, $num) {\n @warn \"Potentially invalid value for #{$map-name}: This map must be in ascending order, but key '#{$key}' has value #{$num} whose unit makes it incomparable to #{$prev-num}, the value of the previous key '#{$prev-key}' !\";\n } @else if $prev-num >= $num {\n @warn \"Invalid value for #{$map-name}: This map must be in ascending order, but key '#{$key}' has value #{$num} which isn't greater than #{$prev-num}, the value of the previous key '#{$prev-key}' !\";\n }\n $prev-key: $key;\n $prev-num: $num;\n }\n}\n\n// Starts at zero\n// Used to ensure the min-width of the lowest breakpoint starts at 0.\n@mixin _assert-starts-at-zero($map, $map-name: \"$grid-breakpoints\") {\n @if length($map) > 0 {\n $values: map-values($map);\n $first-value: nth($values, 1);\n @if $first-value != 0 {\n @warn \"First breakpoint in #{$map-name} must start at 0, but starts at #{$first-value}.\";\n }\n }\n}\n\n// Replace `$search` with `$replace` in `$string`\n// Used on our SVG icon backgrounds for custom forms.\n//\n// @author Hugo Giraudel\n// @param {String} $string - Initial string\n// @param {String} $search - Substring to replace\n// @param {String} $replace ('') - New value\n// @return {String} - Updated string\n@function str-replace($string, $search, $replace: \"\") {\n $index: str-index($string, $search);\n\n @if $index {\n @return str-slice($string, 1, $index - 1) + $replace + str-replace(str-slice($string, $index + str-length($search)), $search, $replace);\n }\n\n @return $string;\n}\n\n// See https://codepen.io/kevinweber/pen/dXWoRw\n//\n// Requires the use of quotes around data URIs.\n\n@function escape-svg($string) {\n @if str-index($string, \"data:image/svg+xml\") {\n @each $char, $encoded in $escaped-characters {\n // Do not escape the url brackets\n @if str-index($string, \"url(\") == 1 {\n $string: url(\"#{str-replace(str-slice($string, 6, -3), $char, $encoded)}\");\n } @else {\n $string: str-replace($string, $char, $encoded);\n }\n }\n }\n\n @return $string;\n}\n\n// Color contrast\n@function color-yiq($color, $dark: $yiq-text-dark, $light: $yiq-text-light) {\n $r: red($color);\n $g: green($color);\n $b: blue($color);\n\n $yiq: (($r * 299) + ($g * 587) + ($b * 114)) / 1000;\n\n @if ($yiq >= $yiq-contrasted-threshold) {\n @return $dark;\n } @else {\n @return $light;\n }\n}\n\n// Retrieve color Sass maps\n@function color($key: \"blue\") {\n @return map-get($colors, $key);\n}\n\n@function theme-color($key: \"primary\") {\n @return map-get($theme-colors, $key);\n}\n\n@function gray($key: \"100\") {\n @return map-get($grays, $key);\n}\n\n// Request a theme color level\n@function theme-color-level($color-name: \"primary\", $level: 0) {\n $color: theme-color($color-name);\n $color-base: if($level > 0, $black, $white);\n $level: abs($level);\n\n @return mix($color-base, $color, $level * $theme-color-interval);\n}\n\n// Return valid calc\n@function add($value1, $value2, $return-calc: true) {\n @if $value1 == null {\n @return $value2;\n }\n\n @if $value2 == null {\n @return $value1;\n }\n\n @if type-of($value1) == number and type-of($value2) == number and comparable($value1, $value2) {\n @return $value1 + $value2;\n }\n\n @return if($return-calc == true, calc(#{$value1} + #{$value2}), $value1 + unquote(\" + \") + $value2);\n}\n\n@function subtract($value1, $value2, $return-calc: true) {\n @if $value1 == null and $value2 == null {\n @return null;\n }\n\n @if $value1 == null {\n @return -$value2;\n }\n\n @if $value2 == null {\n @return $value1;\n }\n\n @if type-of($value1) == number and type-of($value2) == number and comparable($value1, $value2) {\n @return $value1 - $value2;\n }\n\n @return if($return-calc == true, calc(#{$value1} - #{$value2}), $value1 + unquote(\" - \") + $value2);\n}\n", + "// Variables\n//\n// Variables should follow the `$component-state-property-size` formula for\n// consistent naming. Ex: $nav-link-disabled-color and $modal-content-box-shadow-xs.\n\n// Color system\n\n$white: #fff !default;\n$gray-100: #f8f9fa !default;\n$gray-200: #e9ecef !default;\n$gray-300: #dee2e6 !default;\n$gray-400: #ced4da !default;\n$gray-500: #adb5bd !default;\n$gray-600: #6c757d !default;\n$gray-700: #495057 !default;\n$gray-800: #343a40 !default;\n$gray-900: #212529 !default;\n$black: #000 !default;\n\n$grays: () !default;\n$grays: map-merge(\n (\n \"100\": $gray-100,\n \"200\": $gray-200,\n \"300\": $gray-300,\n \"400\": $gray-400,\n \"500\": $gray-500,\n \"600\": $gray-600,\n \"700\": $gray-700,\n \"800\": $gray-800,\n \"900\": $gray-900\n ),\n $grays\n);\n\n$blue: #007bff !default;\n$indigo: #6610f2 !default;\n$purple: #6f42c1 !default;\n$pink: #e83e8c !default;\n$red: #dc3545 !default;\n$orange: #fd7e14 !default;\n$yellow: #ffc107 !default;\n$green: #28a745 !default;\n$teal: #20c997 !default;\n$cyan: #17a2b8 !default;\n\n$colors: () !default;\n$colors: map-merge(\n (\n \"blue\": $blue,\n \"indigo\": $indigo,\n \"purple\": $purple,\n \"pink\": $pink,\n \"red\": $red,\n \"orange\": $orange,\n \"yellow\": $yellow,\n \"green\": $green,\n \"teal\": $teal,\n \"cyan\": $cyan,\n \"white\": $white,\n \"gray\": $gray-600,\n \"gray-dark\": $gray-800\n ),\n $colors\n);\n\n$primary: $blue !default;\n$secondary: $gray-600 !default;\n$success: $green !default;\n$info: $cyan !default;\n$warning: $yellow !default;\n$danger: $red !default;\n$light: $gray-100 !default;\n$dark: $gray-800 !default;\n\n$theme-colors: () !default;\n$theme-colors: map-merge(\n (\n \"primary\": $primary,\n \"secondary\": $secondary,\n \"success\": $success,\n \"info\": $info,\n \"warning\": $warning,\n \"danger\": $danger,\n \"light\": $light,\n \"dark\": $dark\n ),\n $theme-colors\n);\n\n// Set a specific jump point for requesting color jumps\n$theme-color-interval: 8% !default;\n\n// The yiq lightness value that determines when the lightness of color changes from \"dark\" to \"light\". Acceptable values are between 0 and 255.\n$yiq-contrasted-threshold: 150 !default;\n\n// Customize the light and dark text colors for use in our YIQ color contrast function.\n$yiq-text-dark: $gray-900 !default;\n$yiq-text-light: $white !default;\n\n// Characters which are escaped by the escape-svg function\n$escaped-characters: (\n (\"<\", \"%3c\"),\n (\">\", \"%3e\"),\n (\"#\", \"%23\"),\n (\"(\", \"%28\"),\n (\")\", \"%29\"),\n) !default;\n\n\n// Options\n//\n// Quickly modify global styling by enabling or disabling optional features.\n\n$enable-caret: true !default;\n$enable-rounded: true !default;\n$enable-shadows: false !default;\n$enable-gradients: false !default;\n$enable-transitions: true !default;\n$enable-prefers-reduced-motion-media-query: true !default;\n$enable-hover-media-query: false !default; // Deprecated, no longer affects any compiled CSS\n$enable-grid-classes: true !default;\n$enable-pointer-cursor-for-buttons: true !default;\n$enable-print-styles: true !default;\n$enable-responsive-font-sizes: false !default;\n$enable-validation-icons: true !default;\n$enable-deprecation-messages: true !default;\n\n\n// Spacing\n//\n// Control the default styling of most Bootstrap elements by modifying these\n// variables. Mostly focused on spacing.\n// You can add more entries to the $spacers map, should you need more variation.\n\n$spacer: 1rem !default;\n$spacers: () !default;\n$spacers: map-merge(\n (\n 0: 0,\n 1: ($spacer * .25),\n 2: ($spacer * .5),\n 3: $spacer,\n 4: ($spacer * 1.5),\n 5: ($spacer * 3)\n ),\n $spacers\n);\n\n// This variable affects the `.h-*` and `.w-*` classes.\n$sizes: () !default;\n$sizes: map-merge(\n (\n 25: 25%,\n 50: 50%,\n 75: 75%,\n 100: 100%,\n auto: auto\n ),\n $sizes\n);\n\n\n// Body\n//\n// Settings for the `` element.\n\n$body-bg: $white !default;\n$body-color: $gray-900 !default;\n\n\n// Links\n//\n// Style anchor elements.\n\n$link-color: theme-color(\"primary\") !default;\n$link-decoration: none !default;\n$link-hover-color: darken($link-color, 15%) !default;\n$link-hover-decoration: underline !default;\n// Darken percentage for links with `.text-*` class (e.g. `.text-success`)\n$emphasized-link-hover-darken-percentage: 15% !default;\n\n// Paragraphs\n//\n// Style p element.\n\n$paragraph-margin-bottom: 1rem !default;\n\n\n// Grid breakpoints\n//\n// Define the minimum dimensions at which your layout will change,\n// adapting to different screen sizes, for use in media queries.\n\n$grid-breakpoints: (\n xs: 0,\n sm: 576px,\n md: 768px,\n lg: 992px,\n xl: 1200px\n) !default;\n\n@include _assert-ascending($grid-breakpoints, \"$grid-breakpoints\");\n@include _assert-starts-at-zero($grid-breakpoints, \"$grid-breakpoints\");\n\n\n// Grid containers\n//\n// Define the maximum width of `.container` for different screen sizes.\n\n$container-max-widths: (\n sm: 540px,\n md: 720px,\n lg: 960px,\n xl: 1140px\n) !default;\n\n@include _assert-ascending($container-max-widths, \"$container-max-widths\");\n\n\n// Grid columns\n//\n// Set the number of columns and specify the width of the gutters.\n\n$grid-columns: 12 !default;\n$grid-gutter-width: 30px !default;\n$grid-row-columns: 6 !default;\n\n\n// Components\n//\n// Define common padding and border radius sizes and more.\n\n$line-height-lg: 1.5 !default;\n$line-height-sm: 1.5 !default;\n\n$border-width: 1px !default;\n$border-color: $gray-300 !default;\n\n$border-radius: .25rem !default;\n$border-radius-lg: .3rem !default;\n$border-radius-sm: .2rem !default;\n\n$rounded-pill: 50rem !default;\n\n$box-shadow-sm: 0 .125rem .25rem rgba($black, .075) !default;\n$box-shadow: 0 .5rem 1rem rgba($black, .15) !default;\n$box-shadow-lg: 0 1rem 3rem rgba($black, .175) !default;\n\n$component-active-color: $white !default;\n$component-active-bg: theme-color(\"primary\") !default;\n\n$caret-width: .3em !default;\n$caret-vertical-align: $caret-width * .85 !default;\n$caret-spacing: $caret-width * .85 !default;\n\n$transition-base: all .2s ease-in-out !default;\n$transition-fade: opacity .15s linear !default;\n$transition-collapse: height .35s ease !default;\n\n$embed-responsive-aspect-ratios: () !default;\n$embed-responsive-aspect-ratios: join(\n (\n (21 9),\n (16 9),\n (4 3),\n (1 1),\n ),\n $embed-responsive-aspect-ratios\n);\n\n// Typography\n//\n// Font, line-height, and color for body text, headings, and more.\n\n// stylelint-disable value-keyword-case\n$font-family-sans-serif: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, \"Helvetica Neue\", Arial, \"Noto Sans\", sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\", \"Noto Color Emoji\" !default;\n$font-family-monospace: SFMono-Regular, Menlo, Monaco, Consolas, \"Liberation Mono\", \"Courier New\", monospace !default;\n$font-family-base: $font-family-sans-serif !default;\n// stylelint-enable value-keyword-case\n\n$font-size-base: 1rem !default; // Assumes the browser default, typically `16px`\n$font-size-lg: $font-size-base * 1.25 !default;\n$font-size-sm: $font-size-base * .875 !default;\n\n$font-weight-lighter: lighter !default;\n$font-weight-light: 300 !default;\n$font-weight-normal: 400 !default;\n$font-weight-bold: 700 !default;\n$font-weight-bolder: bolder !default;\n\n$font-weight-base: $font-weight-normal !default;\n$line-height-base: 1.5 !default;\n\n$h1-font-size: $font-size-base * 2.5 !default;\n$h2-font-size: $font-size-base * 2 !default;\n$h3-font-size: $font-size-base * 1.75 !default;\n$h4-font-size: $font-size-base * 1.5 !default;\n$h5-font-size: $font-size-base * 1.25 !default;\n$h6-font-size: $font-size-base !default;\n\n$headings-margin-bottom: $spacer / 2 !default;\n$headings-font-family: null !default;\n$headings-font-weight: 500 !default;\n$headings-line-height: 1.2 !default;\n$headings-color: null !default;\n\n$display1-size: 6rem !default;\n$display2-size: 5.5rem !default;\n$display3-size: 4.5rem !default;\n$display4-size: 3.5rem !default;\n\n$display1-weight: 300 !default;\n$display2-weight: 300 !default;\n$display3-weight: 300 !default;\n$display4-weight: 300 !default;\n$display-line-height: $headings-line-height !default;\n\n$lead-font-size: $font-size-base * 1.25 !default;\n$lead-font-weight: 300 !default;\n\n$small-font-size: 80% !default;\n\n$text-muted: $gray-600 !default;\n\n$blockquote-small-color: $gray-600 !default;\n$blockquote-small-font-size: $small-font-size !default;\n$blockquote-font-size: $font-size-base * 1.25 !default;\n\n$hr-border-color: rgba($black, .1) !default;\n$hr-border-width: $border-width !default;\n\n$mark-padding: .2em !default;\n\n$dt-font-weight: $font-weight-bold !default;\n\n$kbd-box-shadow: inset 0 -.1rem 0 rgba($black, .25) !default;\n$nested-kbd-font-weight: $font-weight-bold !default;\n\n$list-inline-padding: .5rem !default;\n\n$mark-bg: #fcf8e3 !default;\n\n$hr-margin-y: $spacer !default;\n\n\n// Tables\n//\n// Customizes the `.table` component with basic values, each used across all table variations.\n\n$table-cell-padding: .75rem !default;\n$table-cell-padding-sm: .3rem !default;\n\n$table-color: $body-color !default;\n$table-bg: null !default;\n$table-accent-bg: rgba($black, .05) !default;\n$table-hover-color: $table-color !default;\n$table-hover-bg: rgba($black, .075) !default;\n$table-active-bg: $table-hover-bg !default;\n\n$table-border-width: $border-width !default;\n$table-border-color: $border-color !default;\n\n$table-head-bg: $gray-200 !default;\n$table-head-color: $gray-700 !default;\n$table-th-font-weight: null !default;\n\n$table-dark-color: $white !default;\n$table-dark-bg: $gray-800 !default;\n$table-dark-accent-bg: rgba($white, .05) !default;\n$table-dark-hover-color: $table-dark-color !default;\n$table-dark-hover-bg: rgba($white, .075) !default;\n$table-dark-border-color: lighten($table-dark-bg, 7.5%) !default;\n\n$table-striped-order: odd !default;\n\n$table-caption-color: $text-muted !default;\n\n$table-bg-level: -9 !default;\n$table-border-level: -6 !default;\n\n\n// Buttons + Forms\n//\n// Shared variables that are reassigned to `$input-` and `$btn-` specific variables.\n\n$input-btn-padding-y: .375rem !default;\n$input-btn-padding-x: .75rem !default;\n$input-btn-font-family: null !default;\n$input-btn-font-size: $font-size-base !default;\n$input-btn-line-height: $line-height-base !default;\n\n$input-btn-focus-width: .2rem !default;\n$input-btn-focus-color: rgba($component-active-bg, .25) !default;\n$input-btn-focus-box-shadow: 0 0 0 $input-btn-focus-width $input-btn-focus-color !default;\n\n$input-btn-padding-y-sm: .25rem !default;\n$input-btn-padding-x-sm: .5rem !default;\n$input-btn-font-size-sm: $font-size-sm !default;\n$input-btn-line-height-sm: $line-height-sm !default;\n\n$input-btn-padding-y-lg: .5rem !default;\n$input-btn-padding-x-lg: 1rem !default;\n$input-btn-font-size-lg: $font-size-lg !default;\n$input-btn-line-height-lg: $line-height-lg !default;\n\n$input-btn-border-width: $border-width !default;\n\n\n// Buttons\n//\n// For each of Bootstrap's buttons, define text, background, and border color.\n\n$btn-padding-y: $input-btn-padding-y !default;\n$btn-padding-x: $input-btn-padding-x !default;\n$btn-font-family: $input-btn-font-family !default;\n$btn-font-size: $input-btn-font-size !default;\n$btn-line-height: $input-btn-line-height !default;\n$btn-white-space: null !default; // Set to `nowrap` to prevent text wrapping\n\n$btn-padding-y-sm: $input-btn-padding-y-sm !default;\n$btn-padding-x-sm: $input-btn-padding-x-sm !default;\n$btn-font-size-sm: $input-btn-font-size-sm !default;\n$btn-line-height-sm: $input-btn-line-height-sm !default;\n\n$btn-padding-y-lg: $input-btn-padding-y-lg !default;\n$btn-padding-x-lg: $input-btn-padding-x-lg !default;\n$btn-font-size-lg: $input-btn-font-size-lg !default;\n$btn-line-height-lg: $input-btn-line-height-lg !default;\n\n$btn-border-width: $input-btn-border-width !default;\n\n$btn-font-weight: $font-weight-normal !default;\n$btn-box-shadow: inset 0 1px 0 rgba($white, .15), 0 1px 1px rgba($black, .075) !default;\n$btn-focus-width: $input-btn-focus-width !default;\n$btn-focus-box-shadow: $input-btn-focus-box-shadow !default;\n$btn-disabled-opacity: .65 !default;\n$btn-active-box-shadow: inset 0 3px 5px rgba($black, .125) !default;\n\n$btn-link-disabled-color: $gray-600 !default;\n\n$btn-block-spacing-y: .5rem !default;\n\n// Allows for customizing button radius independently from global border radius\n$btn-border-radius: $border-radius !default;\n$btn-border-radius-lg: $border-radius-lg !default;\n$btn-border-radius-sm: $border-radius-sm !default;\n\n$btn-transition: color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n\n// Forms\n\n$label-margin-bottom: .5rem !default;\n\n$input-padding-y: $input-btn-padding-y !default;\n$input-padding-x: $input-btn-padding-x !default;\n$input-font-family: $input-btn-font-family !default;\n$input-font-size: $input-btn-font-size !default;\n$input-font-weight: $font-weight-base !default;\n$input-line-height: $input-btn-line-height !default;\n\n$input-padding-y-sm: $input-btn-padding-y-sm !default;\n$input-padding-x-sm: $input-btn-padding-x-sm !default;\n$input-font-size-sm: $input-btn-font-size-sm !default;\n$input-line-height-sm: $input-btn-line-height-sm !default;\n\n$input-padding-y-lg: $input-btn-padding-y-lg !default;\n$input-padding-x-lg: $input-btn-padding-x-lg !default;\n$input-font-size-lg: $input-btn-font-size-lg !default;\n$input-line-height-lg: $input-btn-line-height-lg !default;\n\n$input-bg: $white !default;\n$input-disabled-bg: $gray-200 !default;\n\n$input-color: $gray-700 !default;\n$input-border-color: $gray-400 !default;\n$input-border-width: $input-btn-border-width !default;\n$input-box-shadow: inset 0 1px 1px rgba($black, .075) !default;\n\n$input-border-radius: $border-radius !default;\n$input-border-radius-lg: $border-radius-lg !default;\n$input-border-radius-sm: $border-radius-sm !default;\n\n$input-focus-bg: $input-bg !default;\n$input-focus-border-color: lighten($component-active-bg, 25%) !default;\n$input-focus-color: $input-color !default;\n$input-focus-width: $input-btn-focus-width !default;\n$input-focus-box-shadow: $input-btn-focus-box-shadow !default;\n\n$input-placeholder-color: $gray-600 !default;\n$input-plaintext-color: $body-color !default;\n\n$input-height-border: $input-border-width * 2 !default;\n\n$input-height-inner: add($input-line-height * 1em, $input-padding-y * 2) !default;\n$input-height-inner-half: add($input-line-height * .5em, $input-padding-y) !default;\n$input-height-inner-quarter: add($input-line-height * .25em, $input-padding-y / 2) !default;\n\n$input-height: add($input-line-height * 1em, add($input-padding-y * 2, $input-height-border, false)) !default;\n$input-height-sm: add($input-line-height-sm * 1em, add($input-padding-y-sm * 2, $input-height-border, false)) !default;\n$input-height-lg: add($input-line-height-lg * 1em, add($input-padding-y-lg * 2, $input-height-border, false)) !default;\n\n$input-transition: border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$form-text-margin-top: .25rem !default;\n\n$form-check-input-gutter: 1.25rem !default;\n$form-check-input-margin-y: .3rem !default;\n$form-check-input-margin-x: .25rem !default;\n\n$form-check-inline-margin-x: .75rem !default;\n$form-check-inline-input-margin-x: .3125rem !default;\n\n$form-grid-gutter-width: 10px !default;\n$form-group-margin-bottom: 1rem !default;\n\n$input-group-addon-color: $input-color !default;\n$input-group-addon-bg: $gray-200 !default;\n$input-group-addon-border-color: $input-border-color !default;\n\n$custom-forms-transition: background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$custom-control-gutter: .5rem !default;\n$custom-control-spacer-x: 1rem !default;\n$custom-control-cursor: null !default;\n\n$custom-control-indicator-size: 1rem !default;\n$custom-control-indicator-bg: $input-bg !default;\n\n$custom-control-indicator-bg-size: 50% 50% !default;\n$custom-control-indicator-box-shadow: $input-box-shadow !default;\n$custom-control-indicator-border-color: $gray-500 !default;\n$custom-control-indicator-border-width: $input-border-width !default;\n\n$custom-control-label-color: null !default;\n\n$custom-control-indicator-disabled-bg: $input-disabled-bg !default;\n$custom-control-label-disabled-color: $gray-600 !default;\n\n$custom-control-indicator-checked-color: $component-active-color !default;\n$custom-control-indicator-checked-bg: $component-active-bg !default;\n$custom-control-indicator-checked-disabled-bg: rgba(theme-color(\"primary\"), .5) !default;\n$custom-control-indicator-checked-box-shadow: null !default;\n$custom-control-indicator-checked-border-color: $custom-control-indicator-checked-bg !default;\n\n$custom-control-indicator-focus-box-shadow: $input-focus-box-shadow !default;\n$custom-control-indicator-focus-border-color: $input-focus-border-color !default;\n\n$custom-control-indicator-active-color: $component-active-color !default;\n$custom-control-indicator-active-bg: lighten($component-active-bg, 35%) !default;\n$custom-control-indicator-active-box-shadow: null !default;\n$custom-control-indicator-active-border-color: $custom-control-indicator-active-bg !default;\n\n$custom-checkbox-indicator-border-radius: $border-radius !default;\n$custom-checkbox-indicator-icon-checked: url(\"data:image/svg+xml,\") !default;\n\n$custom-checkbox-indicator-indeterminate-bg: $component-active-bg !default;\n$custom-checkbox-indicator-indeterminate-color: $custom-control-indicator-checked-color !default;\n$custom-checkbox-indicator-icon-indeterminate: url(\"data:image/svg+xml,\") !default;\n$custom-checkbox-indicator-indeterminate-box-shadow: null !default;\n$custom-checkbox-indicator-indeterminate-border-color: $custom-checkbox-indicator-indeterminate-bg !default;\n\n$custom-radio-indicator-border-radius: 50% !default;\n$custom-radio-indicator-icon-checked: url(\"data:image/svg+xml,\") !default;\n\n$custom-switch-width: $custom-control-indicator-size * 1.75 !default;\n$custom-switch-indicator-border-radius: $custom-control-indicator-size / 2 !default;\n$custom-switch-indicator-size: subtract($custom-control-indicator-size, $custom-control-indicator-border-width * 4) !default;\n\n$custom-select-padding-y: $input-padding-y !default;\n$custom-select-padding-x: $input-padding-x !default;\n$custom-select-font-family: $input-font-family !default;\n$custom-select-font-size: $input-font-size !default;\n$custom-select-height: $input-height !default;\n$custom-select-indicator-padding: 1rem !default; // Extra padding to account for the presence of the background-image based indicator\n$custom-select-font-weight: $input-font-weight !default;\n$custom-select-line-height: $input-line-height !default;\n$custom-select-color: $input-color !default;\n$custom-select-disabled-color: $gray-600 !default;\n$custom-select-bg: $input-bg !default;\n$custom-select-disabled-bg: $gray-200 !default;\n$custom-select-bg-size: 8px 10px !default; // In pixels because image dimensions\n$custom-select-indicator-color: $gray-800 !default;\n$custom-select-indicator: url(\"data:image/svg+xml,\") !default;\n$custom-select-background: escape-svg($custom-select-indicator) no-repeat right $custom-select-padding-x center / $custom-select-bg-size !default; // Used so we can have multiple background elements (e.g., arrow and feedback icon)\n\n$custom-select-feedback-icon-padding-right: add(1em * .75, (2 * $custom-select-padding-y * .75) + $custom-select-padding-x + $custom-select-indicator-padding) !default;\n$custom-select-feedback-icon-position: center right ($custom-select-padding-x + $custom-select-indicator-padding) !default;\n$custom-select-feedback-icon-size: $input-height-inner-half $input-height-inner-half !default;\n\n$custom-select-border-width: $input-border-width !default;\n$custom-select-border-color: $input-border-color !default;\n$custom-select-border-radius: $border-radius !default;\n$custom-select-box-shadow: inset 0 1px 2px rgba($black, .075) !default;\n\n$custom-select-focus-border-color: $input-focus-border-color !default;\n$custom-select-focus-width: $input-focus-width !default;\n$custom-select-focus-box-shadow: 0 0 0 $custom-select-focus-width $input-btn-focus-color !default;\n\n$custom-select-padding-y-sm: $input-padding-y-sm !default;\n$custom-select-padding-x-sm: $input-padding-x-sm !default;\n$custom-select-font-size-sm: $input-font-size-sm !default;\n$custom-select-height-sm: $input-height-sm !default;\n\n$custom-select-padding-y-lg: $input-padding-y-lg !default;\n$custom-select-padding-x-lg: $input-padding-x-lg !default;\n$custom-select-font-size-lg: $input-font-size-lg !default;\n$custom-select-height-lg: $input-height-lg !default;\n\n$custom-range-track-width: 100% !default;\n$custom-range-track-height: .5rem !default;\n$custom-range-track-cursor: pointer !default;\n$custom-range-track-bg: $gray-300 !default;\n$custom-range-track-border-radius: 1rem !default;\n$custom-range-track-box-shadow: inset 0 .25rem .25rem rgba($black, .1) !default;\n\n$custom-range-thumb-width: 1rem !default;\n$custom-range-thumb-height: $custom-range-thumb-width !default;\n$custom-range-thumb-bg: $component-active-bg !default;\n$custom-range-thumb-border: 0 !default;\n$custom-range-thumb-border-radius: 1rem !default;\n$custom-range-thumb-box-shadow: 0 .1rem .25rem rgba($black, .1) !default;\n$custom-range-thumb-focus-box-shadow: 0 0 0 1px $body-bg, $input-focus-box-shadow !default;\n$custom-range-thumb-focus-box-shadow-width: $input-focus-width !default; // For focus box shadow issue in IE/Edge\n$custom-range-thumb-active-bg: lighten($component-active-bg, 35%) !default;\n$custom-range-thumb-disabled-bg: $gray-500 !default;\n\n$custom-file-height: $input-height !default;\n$custom-file-height-inner: $input-height-inner !default;\n$custom-file-focus-border-color: $input-focus-border-color !default;\n$custom-file-focus-box-shadow: $input-focus-box-shadow !default;\n$custom-file-disabled-bg: $input-disabled-bg !default;\n\n$custom-file-padding-y: $input-padding-y !default;\n$custom-file-padding-x: $input-padding-x !default;\n$custom-file-line-height: $input-line-height !default;\n$custom-file-font-family: $input-font-family !default;\n$custom-file-font-weight: $input-font-weight !default;\n$custom-file-color: $input-color !default;\n$custom-file-bg: $input-bg !default;\n$custom-file-border-width: $input-border-width !default;\n$custom-file-border-color: $input-border-color !default;\n$custom-file-border-radius: $input-border-radius !default;\n$custom-file-box-shadow: $input-box-shadow !default;\n$custom-file-button-color: $custom-file-color !default;\n$custom-file-button-bg: $input-group-addon-bg !default;\n$custom-file-text: (\n en: \"Browse\"\n) !default;\n\n\n// Form validation\n\n$form-feedback-margin-top: $form-text-margin-top !default;\n$form-feedback-font-size: $small-font-size !default;\n$form-feedback-valid-color: theme-color(\"success\") !default;\n$form-feedback-invalid-color: theme-color(\"danger\") !default;\n\n$form-feedback-icon-valid-color: $form-feedback-valid-color !default;\n$form-feedback-icon-valid: url(\"data:image/svg+xml,\") !default;\n$form-feedback-icon-invalid-color: $form-feedback-invalid-color !default;\n$form-feedback-icon-invalid: url(\"data:image/svg+xml,\") !default;\n\n$form-validation-states: () !default;\n$form-validation-states: map-merge(\n (\n \"valid\": (\n \"color\": $form-feedback-valid-color,\n \"icon\": $form-feedback-icon-valid\n ),\n \"invalid\": (\n \"color\": $form-feedback-invalid-color,\n \"icon\": $form-feedback-icon-invalid\n ),\n ),\n $form-validation-states\n);\n\n// Z-index master list\n//\n// Warning: Avoid customizing these values. They're used for a bird's eye view\n// of components dependent on the z-axis and are designed to all work together.\n\n$zindex-dropdown: 1000 !default;\n$zindex-sticky: 1020 !default;\n$zindex-fixed: 1030 !default;\n$zindex-modal-backdrop: 1040 !default;\n$zindex-modal: 1050 !default;\n$zindex-popover: 1060 !default;\n$zindex-tooltip: 1070 !default;\n\n\n// Navs\n\n$nav-link-padding-y: .5rem !default;\n$nav-link-padding-x: 1rem !default;\n$nav-link-disabled-color: $gray-600 !default;\n\n$nav-tabs-border-color: $gray-300 !default;\n$nav-tabs-border-width: $border-width !default;\n$nav-tabs-border-radius: $border-radius !default;\n$nav-tabs-link-hover-border-color: $gray-200 $gray-200 $nav-tabs-border-color !default;\n$nav-tabs-link-active-color: $gray-700 !default;\n$nav-tabs-link-active-bg: $body-bg !default;\n$nav-tabs-link-active-border-color: $gray-300 $gray-300 $nav-tabs-link-active-bg !default;\n\n$nav-pills-border-radius: $border-radius !default;\n$nav-pills-link-active-color: $component-active-color !default;\n$nav-pills-link-active-bg: $component-active-bg !default;\n\n$nav-divider-color: $gray-200 !default;\n$nav-divider-margin-y: $spacer / 2 !default;\n\n\n// Navbar\n\n$navbar-padding-y: $spacer / 2 !default;\n$navbar-padding-x: $spacer !default;\n\n$navbar-nav-link-padding-x: .5rem !default;\n\n$navbar-brand-font-size: $font-size-lg !default;\n// Compute the navbar-brand padding-y so the navbar-brand will have the same height as navbar-text and nav-link\n$nav-link-height: $font-size-base * $line-height-base + $nav-link-padding-y * 2 !default;\n$navbar-brand-height: $navbar-brand-font-size * $line-height-base !default;\n$navbar-brand-padding-y: ($nav-link-height - $navbar-brand-height) / 2 !default;\n\n$navbar-toggler-padding-y: .25rem !default;\n$navbar-toggler-padding-x: .75rem !default;\n$navbar-toggler-font-size: $font-size-lg !default;\n$navbar-toggler-border-radius: $btn-border-radius !default;\n\n$navbar-dark-color: rgba($white, .5) !default;\n$navbar-dark-hover-color: rgba($white, .75) !default;\n$navbar-dark-active-color: $white !default;\n$navbar-dark-disabled-color: rgba($white, .25) !default;\n$navbar-dark-toggler-icon-bg: url(\"data:image/svg+xml,\") !default;\n$navbar-dark-toggler-border-color: rgba($white, .1) !default;\n\n$navbar-light-color: rgba($black, .5) !default;\n$navbar-light-hover-color: rgba($black, .7) !default;\n$navbar-light-active-color: rgba($black, .9) !default;\n$navbar-light-disabled-color: rgba($black, .3) !default;\n$navbar-light-toggler-icon-bg: url(\"data:image/svg+xml,\") !default;\n$navbar-light-toggler-border-color: rgba($black, .1) !default;\n\n$navbar-light-brand-color: $navbar-light-active-color !default;\n$navbar-light-brand-hover-color: $navbar-light-active-color !default;\n$navbar-dark-brand-color: $navbar-dark-active-color !default;\n$navbar-dark-brand-hover-color: $navbar-dark-active-color !default;\n\n\n// Dropdowns\n//\n// Dropdown menu container and contents.\n\n$dropdown-min-width: 10rem !default;\n$dropdown-padding-x: 0 !default;\n$dropdown-padding-y: .5rem !default;\n$dropdown-spacer: .125rem !default;\n$dropdown-font-size: $font-size-base !default;\n$dropdown-color: $body-color !default;\n$dropdown-bg: $white !default;\n$dropdown-border-color: rgba($black, .15) !default;\n$dropdown-border-radius: $border-radius !default;\n$dropdown-border-width: $border-width !default;\n$dropdown-inner-border-radius: subtract($dropdown-border-radius, $dropdown-border-width) !default;\n$dropdown-divider-bg: $gray-200 !default;\n$dropdown-divider-margin-y: $nav-divider-margin-y !default;\n$dropdown-box-shadow: 0 .5rem 1rem rgba($black, .175) !default;\n\n$dropdown-link-color: $gray-900 !default;\n$dropdown-link-hover-color: darken($gray-900, 5%) !default;\n$dropdown-link-hover-bg: $gray-100 !default;\n\n$dropdown-link-active-color: $component-active-color !default;\n$dropdown-link-active-bg: $component-active-bg !default;\n\n$dropdown-link-disabled-color: $gray-600 !default;\n\n$dropdown-item-padding-y: .25rem !default;\n$dropdown-item-padding-x: 1.5rem !default;\n\n$dropdown-header-color: $gray-600 !default;\n$dropdown-header-padding: $dropdown-padding-y $dropdown-item-padding-x !default;\n\n\n// Pagination\n\n$pagination-padding-y: .5rem !default;\n$pagination-padding-x: .75rem !default;\n$pagination-padding-y-sm: .25rem !default;\n$pagination-padding-x-sm: .5rem !default;\n$pagination-padding-y-lg: .75rem !default;\n$pagination-padding-x-lg: 1.5rem !default;\n$pagination-line-height: 1.25 !default;\n\n$pagination-color: $link-color !default;\n$pagination-bg: $white !default;\n$pagination-border-width: $border-width !default;\n$pagination-border-color: $gray-300 !default;\n\n$pagination-focus-box-shadow: $input-btn-focus-box-shadow !default;\n$pagination-focus-outline: 0 !default;\n\n$pagination-hover-color: $link-hover-color !default;\n$pagination-hover-bg: $gray-200 !default;\n$pagination-hover-border-color: $gray-300 !default;\n\n$pagination-active-color: $component-active-color !default;\n$pagination-active-bg: $component-active-bg !default;\n$pagination-active-border-color: $pagination-active-bg !default;\n\n$pagination-disabled-color: $gray-600 !default;\n$pagination-disabled-bg: $white !default;\n$pagination-disabled-border-color: $gray-300 !default;\n\n\n// Jumbotron\n\n$jumbotron-padding: 2rem !default;\n$jumbotron-color: null !default;\n$jumbotron-bg: $gray-200 !default;\n\n\n// Cards\n\n$card-spacer-y: .75rem !default;\n$card-spacer-x: 1.25rem !default;\n$card-border-width: $border-width !default;\n$card-border-radius: $border-radius !default;\n$card-border-color: rgba($black, .125) !default;\n$card-inner-border-radius: subtract($card-border-radius, $card-border-width) !default;\n$card-cap-bg: rgba($black, .03) !default;\n$card-cap-color: null !default;\n$card-height: null !default;\n$card-color: null !default;\n$card-bg: $white !default;\n\n$card-img-overlay-padding: 1.25rem !default;\n\n$card-group-margin: $grid-gutter-width / 2 !default;\n$card-deck-margin: $card-group-margin !default;\n\n$card-columns-count: 3 !default;\n$card-columns-gap: 1.25rem !default;\n$card-columns-margin: $card-spacer-y !default;\n\n\n// Tooltips\n\n$tooltip-font-size: $font-size-sm !default;\n$tooltip-max-width: 200px !default;\n$tooltip-color: $white !default;\n$tooltip-bg: $black !default;\n$tooltip-border-radius: $border-radius !default;\n$tooltip-opacity: .9 !default;\n$tooltip-padding-y: .25rem !default;\n$tooltip-padding-x: .5rem !default;\n$tooltip-margin: 0 !default;\n\n$tooltip-arrow-width: .8rem !default;\n$tooltip-arrow-height: .4rem !default;\n$tooltip-arrow-color: $tooltip-bg !default;\n\n// Form tooltips must come after regular tooltips\n$form-feedback-tooltip-padding-y: $tooltip-padding-y !default;\n$form-feedback-tooltip-padding-x: $tooltip-padding-x !default;\n$form-feedback-tooltip-font-size: $tooltip-font-size !default;\n$form-feedback-tooltip-line-height: $line-height-base !default;\n$form-feedback-tooltip-opacity: $tooltip-opacity !default;\n$form-feedback-tooltip-border-radius: $tooltip-border-radius !default;\n\n\n// Popovers\n\n$popover-font-size: $font-size-sm !default;\n$popover-bg: $white !default;\n$popover-max-width: 276px !default;\n$popover-border-width: $border-width !default;\n$popover-border-color: rgba($black, .2) !default;\n$popover-border-radius: $border-radius-lg !default;\n$popover-inner-border-radius: subtract($popover-border-radius, $popover-border-width) !default;\n$popover-box-shadow: 0 .25rem .5rem rgba($black, .2) !default;\n\n$popover-header-bg: darken($popover-bg, 3%) !default;\n$popover-header-color: $headings-color !default;\n$popover-header-padding-y: .5rem !default;\n$popover-header-padding-x: .75rem !default;\n\n$popover-body-color: $body-color !default;\n$popover-body-padding-y: $popover-header-padding-y !default;\n$popover-body-padding-x: $popover-header-padding-x !default;\n\n$popover-arrow-width: 1rem !default;\n$popover-arrow-height: .5rem !default;\n$popover-arrow-color: $popover-bg !default;\n\n$popover-arrow-outer-color: fade-in($popover-border-color, .05) !default;\n\n\n// Toasts\n\n$toast-max-width: 350px !default;\n$toast-padding-x: .75rem !default;\n$toast-padding-y: .25rem !default;\n$toast-font-size: .875rem !default;\n$toast-color: null !default;\n$toast-background-color: rgba($white, .85) !default;\n$toast-border-width: 1px !default;\n$toast-border-color: rgba(0, 0, 0, .1) !default;\n$toast-border-radius: .25rem !default;\n$toast-box-shadow: 0 .25rem .75rem rgba($black, .1) !default;\n\n$toast-header-color: $gray-600 !default;\n$toast-header-background-color: rgba($white, .85) !default;\n$toast-header-border-color: rgba(0, 0, 0, .05) !default;\n\n\n// Badges\n\n$badge-font-size: 75% !default;\n$badge-font-weight: $font-weight-bold !default;\n$badge-padding-y: .25em !default;\n$badge-padding-x: .4em !default;\n$badge-border-radius: $border-radius !default;\n\n$badge-transition: $btn-transition !default;\n$badge-focus-width: $input-btn-focus-width !default;\n\n$badge-pill-padding-x: .6em !default;\n// Use a higher than normal value to ensure completely rounded edges when\n// customizing padding or font-size on labels.\n$badge-pill-border-radius: 10rem !default;\n\n\n// Modals\n\n// Padding applied to the modal body\n$modal-inner-padding: 1rem !default;\n\n// Margin between elements in footer, must be lower than or equal to 2 * $modal-inner-padding\n$modal-footer-margin-between: .5rem !default;\n\n$modal-dialog-margin: .5rem !default;\n$modal-dialog-margin-y-sm-up: 1.75rem !default;\n\n$modal-title-line-height: $line-height-base !default;\n\n$modal-content-color: null !default;\n$modal-content-bg: $white !default;\n$modal-content-border-color: rgba($black, .2) !default;\n$modal-content-border-width: $border-width !default;\n$modal-content-border-radius: $border-radius-lg !default;\n$modal-content-inner-border-radius: subtract($modal-content-border-radius, $modal-content-border-width) !default;\n$modal-content-box-shadow-xs: 0 .25rem .5rem rgba($black, .5) !default;\n$modal-content-box-shadow-sm-up: 0 .5rem 1rem rgba($black, .5) !default;\n\n$modal-backdrop-bg: $black !default;\n$modal-backdrop-opacity: .5 !default;\n$modal-header-border-color: $border-color !default;\n$modal-footer-border-color: $modal-header-border-color !default;\n$modal-header-border-width: $modal-content-border-width !default;\n$modal-footer-border-width: $modal-header-border-width !default;\n$modal-header-padding-y: 1rem !default;\n$modal-header-padding-x: 1rem !default;\n$modal-header-padding: $modal-header-padding-y $modal-header-padding-x !default; // Keep this for backwards compatibility\n\n$modal-xl: 1140px !default;\n$modal-lg: 800px !default;\n$modal-md: 500px !default;\n$modal-sm: 300px !default;\n\n$modal-fade-transform: translate(0, -50px) !default;\n$modal-show-transform: none !default;\n$modal-transition: transform .3s ease-out !default;\n$modal-scale-transform: scale(1.02) !default;\n\n\n// Alerts\n//\n// Define alert colors, border radius, and padding.\n\n$alert-padding-y: .75rem !default;\n$alert-padding-x: 1.25rem !default;\n$alert-margin-bottom: 1rem !default;\n$alert-border-radius: $border-radius !default;\n$alert-link-font-weight: $font-weight-bold !default;\n$alert-border-width: $border-width !default;\n\n$alert-bg-level: -10 !default;\n$alert-border-level: -9 !default;\n$alert-color-level: 6 !default;\n\n\n// Progress bars\n\n$progress-height: 1rem !default;\n$progress-font-size: $font-size-base * .75 !default;\n$progress-bg: $gray-200 !default;\n$progress-border-radius: $border-radius !default;\n$progress-box-shadow: inset 0 .1rem .1rem rgba($black, .1) !default;\n$progress-bar-color: $white !default;\n$progress-bar-bg: theme-color(\"primary\") !default;\n$progress-bar-animation-timing: 1s linear infinite !default;\n$progress-bar-transition: width .6s ease !default;\n\n\n// List group\n\n$list-group-color: null !default;\n$list-group-bg: $white !default;\n$list-group-border-color: rgba($black, .125) !default;\n$list-group-border-width: $border-width !default;\n$list-group-border-radius: $border-radius !default;\n\n$list-group-item-padding-y: .75rem !default;\n$list-group-item-padding-x: 1.25rem !default;\n\n$list-group-hover-bg: $gray-100 !default;\n$list-group-active-color: $component-active-color !default;\n$list-group-active-bg: $component-active-bg !default;\n$list-group-active-border-color: $list-group-active-bg !default;\n\n$list-group-disabled-color: $gray-600 !default;\n$list-group-disabled-bg: $list-group-bg !default;\n\n$list-group-action-color: $gray-700 !default;\n$list-group-action-hover-color: $list-group-action-color !default;\n\n$list-group-action-active-color: $body-color !default;\n$list-group-action-active-bg: $gray-200 !default;\n\n\n// Image thumbnails\n\n$thumbnail-padding: .25rem !default;\n$thumbnail-bg: $body-bg !default;\n$thumbnail-border-width: $border-width !default;\n$thumbnail-border-color: $gray-300 !default;\n$thumbnail-border-radius: $border-radius !default;\n$thumbnail-box-shadow: 0 1px 2px rgba($black, .075) !default;\n\n\n// Figures\n\n$figure-caption-font-size: 90% !default;\n$figure-caption-color: $gray-600 !default;\n\n\n// Breadcrumbs\n\n$breadcrumb-font-size: null !default;\n\n$breadcrumb-padding-y: .75rem !default;\n$breadcrumb-padding-x: 1rem !default;\n$breadcrumb-item-padding: .5rem !default;\n\n$breadcrumb-margin-bottom: 1rem !default;\n\n$breadcrumb-bg: $gray-200 !default;\n$breadcrumb-divider-color: $gray-600 !default;\n$breadcrumb-active-color: $gray-600 !default;\n$breadcrumb-divider: quote(\"/\") !default;\n\n$breadcrumb-border-radius: $border-radius !default;\n\n\n// Carousel\n\n$carousel-control-color: $white !default;\n$carousel-control-width: 15% !default;\n$carousel-control-opacity: .5 !default;\n$carousel-control-hover-opacity: .9 !default;\n$carousel-control-transition: opacity .15s ease !default;\n\n$carousel-indicator-width: 30px !default;\n$carousel-indicator-height: 3px !default;\n$carousel-indicator-hit-area-height: 10px !default;\n$carousel-indicator-spacer: 3px !default;\n$carousel-indicator-active-bg: $white !default;\n$carousel-indicator-transition: opacity .6s ease !default;\n\n$carousel-caption-width: 70% !default;\n$carousel-caption-color: $white !default;\n\n$carousel-control-icon-width: 20px !default;\n\n$carousel-control-prev-icon-bg: url(\"data:image/svg+xml,\") !default;\n$carousel-control-next-icon-bg: url(\"data:image/svg+xml,\") !default;\n\n$carousel-transition-duration: .6s !default;\n$carousel-transition: transform $carousel-transition-duration ease-in-out !default; // Define transform transition first if using multiple transitions (e.g., `transform 2s ease, opacity .5s ease-out`)\n\n\n// Spinners\n\n$spinner-width: 2rem !default;\n$spinner-height: $spinner-width !default;\n$spinner-border-width: .25em !default;\n\n$spinner-width-sm: 1rem !default;\n$spinner-height-sm: $spinner-width-sm !default;\n$spinner-border-width-sm: .2em !default;\n\n\n// Close\n\n$close-font-size: $font-size-base * 1.5 !default;\n$close-font-weight: $font-weight-bold !default;\n$close-color: $black !default;\n$close-text-shadow: 0 1px 0 $white !default;\n\n\n// Code\n\n$code-font-size: 87.5% !default;\n$code-color: $pink !default;\n\n$kbd-padding-y: .2rem !default;\n$kbd-padding-x: .4rem !default;\n$kbd-font-size: $code-font-size !default;\n$kbd-color: $white !default;\n$kbd-bg: $gray-900 !default;\n\n$pre-color: $gray-900 !default;\n$pre-scrollable-max-height: 340px !default;\n\n\n// Utilities\n\n$displays: none, inline, inline-block, block, table, table-row, table-cell, flex, inline-flex !default;\n$overflows: auto, hidden !default;\n$positions: static, relative, absolute, fixed, sticky !default;\n$user-selects: all, auto, none !default;\n\n\n// Printing\n\n$print-page-size: a3 !default;\n$print-body-min-width: map-get($grid-breakpoints, \"lg\") !default;\n", + "// Toggles\n//\n// Used in conjunction with global variables to enable certain theme features.\n\n// Vendor\n@import \"vendor/rfs\";\n\n// Deprecate\n@import \"mixins/deprecate\";\n\n// Utilities\n@import \"mixins/breakpoints\";\n@import \"mixins/hover\";\n@import \"mixins/image\";\n@import \"mixins/badge\";\n@import \"mixins/resize\";\n@import \"mixins/screen-reader\";\n@import \"mixins/size\";\n@import \"mixins/reset-text\";\n@import \"mixins/text-emphasis\";\n@import \"mixins/text-hide\";\n@import \"mixins/text-truncate\";\n@import \"mixins/visibility\";\n\n// Components\n@import \"mixins/alert\";\n@import \"mixins/buttons\";\n@import \"mixins/caret\";\n@import \"mixins/pagination\";\n@import \"mixins/lists\";\n@import \"mixins/list-group\";\n@import \"mixins/nav-divider\";\n@import \"mixins/forms\";\n@import \"mixins/table-row\";\n\n// Skins\n@import \"mixins/background-variant\";\n@import \"mixins/border-radius\";\n@import \"mixins/box-shadow\";\n@import \"mixins/gradients\";\n@import \"mixins/transition\";\n\n// Layout\n@import \"mixins/clearfix\";\n@import \"mixins/grid-framework\";\n@import \"mixins/grid\";\n@import \"mixins/float\";\n", + "// stylelint-disable property-blacklist, scss/dollar-variable-default\n\n// SCSS RFS mixin\n//\n// Automated font-resizing\n//\n// See https://github.com/twbs/rfs\n\n// Configuration\n\n// Base font size\n$rfs-base-font-size: 1.25rem !default;\n$rfs-font-size-unit: rem !default;\n\n// Breakpoint at where font-size starts decreasing if screen width is smaller\n$rfs-breakpoint: 1200px !default;\n$rfs-breakpoint-unit: px !default;\n\n// Resize font-size based on screen height and width\n$rfs-two-dimensional: false !default;\n\n// Factor of decrease\n$rfs-factor: 10 !default;\n\n@if type-of($rfs-factor) != \"number\" or $rfs-factor <= 1 {\n @error \"`#{$rfs-factor}` is not a valid $rfs-factor, it must be greater than 1.\";\n}\n\n// Generate enable or disable classes. Possibilities: false, \"enable\" or \"disable\"\n$rfs-class: false !default;\n\n// 1 rem = $rfs-rem-value px\n$rfs-rem-value: 16 !default;\n\n// Safari iframe resize bug: https://github.com/twbs/rfs/issues/14\n$rfs-safari-iframe-resize-bug-fix: false !default;\n\n// Disable RFS by setting $enable-responsive-font-sizes to false\n$enable-responsive-font-sizes: true !default;\n\n// Cache $rfs-base-font-size unit\n$rfs-base-font-size-unit: unit($rfs-base-font-size);\n\n// Remove px-unit from $rfs-base-font-size for calculations\n@if $rfs-base-font-size-unit == \"px\" {\n $rfs-base-font-size: $rfs-base-font-size / ($rfs-base-font-size * 0 + 1);\n}\n@else if $rfs-base-font-size-unit == \"rem\" {\n $rfs-base-font-size: $rfs-base-font-size / ($rfs-base-font-size * 0 + 1 / $rfs-rem-value);\n}\n\n// Cache $rfs-breakpoint unit to prevent multiple calls\n$rfs-breakpoint-unit-cache: unit($rfs-breakpoint);\n\n// Remove unit from $rfs-breakpoint for calculations\n@if $rfs-breakpoint-unit-cache == \"px\" {\n $rfs-breakpoint: $rfs-breakpoint / ($rfs-breakpoint * 0 + 1);\n}\n@else if $rfs-breakpoint-unit-cache == \"rem\" or $rfs-breakpoint-unit-cache == \"em\" {\n $rfs-breakpoint: $rfs-breakpoint / ($rfs-breakpoint * 0 + 1 / $rfs-rem-value);\n}\n\n// Responsive font-size mixin\n@mixin rfs($fs, $important: false) {\n // Cache $fs unit\n $fs-unit: if(type-of($fs) == \"number\", unit($fs), false);\n\n // Add !important suffix if needed\n $rfs-suffix: if($important, \" !important\", \"\");\n\n // If $fs isn't a number (like inherit) or $fs has a unit (not px or rem, like 1.5em) or $ is 0, just print the value\n @if not $fs-unit or $fs-unit != \"\" and $fs-unit != \"px\" and $fs-unit != \"rem\" or $fs == 0 {\n font-size: #{$fs}#{$rfs-suffix};\n }\n @else {\n // Variables for storing static and fluid rescaling\n $rfs-static: null;\n $rfs-fluid: null;\n\n // Remove px-unit from $fs for calculations\n @if $fs-unit == \"px\" {\n $fs: $fs / ($fs * 0 + 1);\n }\n @else if $fs-unit == \"rem\" {\n $fs: $fs / ($fs * 0 + 1 / $rfs-rem-value);\n }\n\n // Set default font-size\n @if $rfs-font-size-unit == rem {\n $rfs-static: #{$fs / $rfs-rem-value}rem#{$rfs-suffix};\n }\n @else if $rfs-font-size-unit == px {\n $rfs-static: #{$fs}px#{$rfs-suffix};\n }\n @else {\n @error \"`#{$rfs-font-size-unit}` is not a valid unit for $rfs-font-size-unit. Use `px` or `rem`.\";\n }\n\n // Only add media query if font-size is bigger as the minimum font-size\n // If $rfs-factor == 1, no rescaling will take place\n @if $fs > $rfs-base-font-size and $enable-responsive-font-sizes {\n $min-width: null;\n $variable-unit: null;\n\n // Calculate minimum font-size for given font-size\n $fs-min: $rfs-base-font-size + ($fs - $rfs-base-font-size) / $rfs-factor;\n\n // Calculate difference between given font-size and minimum font-size for given font-size\n $fs-diff: $fs - $fs-min;\n\n // Base font-size formatting\n // No need to check if the unit is valid, because we did that before\n $min-width: if($rfs-font-size-unit == rem, #{$fs-min / $rfs-rem-value}rem, #{$fs-min}px);\n\n // If two-dimensional, use smallest of screen width and height\n $variable-unit: if($rfs-two-dimensional, vmin, vw);\n\n // Calculate the variable width between 0 and $rfs-breakpoint\n $variable-width: #{$fs-diff * 100 / $rfs-breakpoint}#{$variable-unit};\n\n // Set the calculated font-size.\n $rfs-fluid: calc(#{$min-width} + #{$variable-width}) #{$rfs-suffix};\n }\n\n // Rendering\n @if $rfs-fluid == null {\n // Only render static font-size if no fluid font-size is available\n font-size: $rfs-static;\n }\n @else {\n $mq-value: null;\n\n // RFS breakpoint formatting\n @if $rfs-breakpoint-unit == em or $rfs-breakpoint-unit == rem {\n $mq-value: #{$rfs-breakpoint / $rfs-rem-value}#{$rfs-breakpoint-unit};\n }\n @else if $rfs-breakpoint-unit == px {\n $mq-value: #{$rfs-breakpoint}px;\n }\n @else {\n @error \"`#{$rfs-breakpoint-unit}` is not a valid unit for $rfs-breakpoint-unit. Use `px`, `em` or `rem`.\";\n }\n\n @if $rfs-class == \"disable\" {\n // Adding an extra class increases specificity,\n // which prevents the media query to override the font size\n &,\n .disable-responsive-font-size &,\n &.disable-responsive-font-size {\n font-size: $rfs-static;\n }\n }\n @else {\n font-size: $rfs-static;\n }\n\n @if $rfs-two-dimensional {\n @media (max-width: #{$mq-value}), (max-height: #{$mq-value}) {\n @if $rfs-class == \"enable\" {\n .enable-responsive-font-size &,\n &.enable-responsive-font-size {\n font-size: $rfs-fluid;\n }\n }\n @else {\n font-size: $rfs-fluid;\n }\n\n @if $rfs-safari-iframe-resize-bug-fix {\n // stylelint-disable-next-line length-zero-no-unit\n min-width: 0vw;\n }\n }\n }\n @else {\n @media (max-width: #{$mq-value}) {\n @if $rfs-class == \"enable\" {\n .enable-responsive-font-size &,\n &.enable-responsive-font-size {\n font-size: $rfs-fluid;\n }\n }\n @else {\n font-size: $rfs-fluid;\n }\n\n @if $rfs-safari-iframe-resize-bug-fix {\n // stylelint-disable-next-line length-zero-no-unit\n min-width: 0vw;\n }\n }\n }\n }\n }\n}\n\n// The font-size & responsive-font-size mixin uses RFS to rescale font sizes\n@mixin font-size($fs, $important: false) {\n @include rfs($fs, $important);\n}\n\n@mixin responsive-font-size($fs, $important: false) {\n @include rfs($fs, $important);\n}\n", + "// Deprecate mixin\n//\n// This mixin can be used to deprecate mixins or functions.\n// `$enable-deprecation-messages` is a global variable, `$ignore-warning` is a variable that can be passed to\n// some deprecated mixins to suppress the warning (for example if the mixin is still be used in the current version of Bootstrap)\n@mixin deprecate($name, $deprecate-version, $remove-version, $ignore-warning: false) {\n @if ($enable-deprecation-messages != false and $ignore-warning != true) {\n @warn \"#{$name} has been deprecated as of #{$deprecate-version}. It will be removed entirely in #{$remove-version}.\";\n }\n}\n", + "// Breakpoint viewport sizes and media queries.\n//\n// Breakpoints are defined as a map of (name: minimum width), order from small to large:\n//\n// (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px)\n//\n// The map defined in the `$grid-breakpoints` global variable is used as the `$breakpoints` argument by default.\n\n// Name of the next breakpoint, or null for the last breakpoint.\n//\n// >> breakpoint-next(sm)\n// md\n// >> breakpoint-next(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px))\n// md\n// >> breakpoint-next(sm, $breakpoint-names: (xs sm md lg xl))\n// md\n@function breakpoint-next($name, $breakpoints: $grid-breakpoints, $breakpoint-names: map-keys($breakpoints)) {\n $n: index($breakpoint-names, $name);\n @return if($n != null and $n < length($breakpoint-names), nth($breakpoint-names, $n + 1), null);\n}\n\n// Minimum breakpoint width. Null for the smallest (first) breakpoint.\n//\n// >> breakpoint-min(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px))\n// 576px\n@function breakpoint-min($name, $breakpoints: $grid-breakpoints) {\n $min: map-get($breakpoints, $name);\n @return if($min != 0, $min, null);\n}\n\n// Maximum breakpoint width. Null for the largest (last) breakpoint.\n// The maximum value is calculated as the minimum of the next one less 0.02px\n// to work around the limitations of `min-` and `max-` prefixes and viewports with fractional widths.\n// See https://www.w3.org/TR/mediaqueries-4/#mq-min-max\n// Uses 0.02px rather than 0.01px to work around a current rounding bug in Safari.\n// See https://bugs.webkit.org/show_bug.cgi?id=178261\n//\n// >> breakpoint-max(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px))\n// 767.98px\n@function breakpoint-max($name, $breakpoints: $grid-breakpoints) {\n $next: breakpoint-next($name, $breakpoints);\n @return if($next, breakpoint-min($next, $breakpoints) - .02, null);\n}\n\n// Returns a blank string if smallest breakpoint, otherwise returns the name with a dash in front.\n// Useful for making responsive utilities.\n//\n// >> breakpoint-infix(xs, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px))\n// \"\" (Returns a blank string)\n// >> breakpoint-infix(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px))\n// \"-sm\"\n@function breakpoint-infix($name, $breakpoints: $grid-breakpoints) {\n @return if(breakpoint-min($name, $breakpoints) == null, \"\", \"-#{$name}\");\n}\n\n// Media of at least the minimum breakpoint width. No query for the smallest breakpoint.\n// Makes the @content apply to the given breakpoint and wider.\n@mixin media-breakpoint-up($name, $breakpoints: $grid-breakpoints) {\n $min: breakpoint-min($name, $breakpoints);\n @if $min {\n @media (min-width: $min) {\n @content;\n }\n } @else {\n @content;\n }\n}\n\n// Media of at most the maximum breakpoint width. No query for the largest breakpoint.\n// Makes the @content apply to the given breakpoint and narrower.\n@mixin media-breakpoint-down($name, $breakpoints: $grid-breakpoints) {\n $max: breakpoint-max($name, $breakpoints);\n @if $max {\n @media (max-width: $max) {\n @content;\n }\n } @else {\n @content;\n }\n}\n\n// Media that spans multiple breakpoint widths.\n// Makes the @content apply between the min and max breakpoints\n@mixin media-breakpoint-between($lower, $upper, $breakpoints: $grid-breakpoints) {\n $min: breakpoint-min($lower, $breakpoints);\n $max: breakpoint-max($upper, $breakpoints);\n\n @if $min != null and $max != null {\n @media (min-width: $min) and (max-width: $max) {\n @content;\n }\n } @else if $max == null {\n @include media-breakpoint-up($lower, $breakpoints) {\n @content;\n }\n } @else if $min == null {\n @include media-breakpoint-down($upper, $breakpoints) {\n @content;\n }\n }\n}\n\n// Media between the breakpoint's minimum and maximum widths.\n// No minimum for the smallest breakpoint, and no maximum for the largest one.\n// Makes the @content apply only to the given breakpoint, not viewports any wider or narrower.\n@mixin media-breakpoint-only($name, $breakpoints: $grid-breakpoints) {\n $min: breakpoint-min($name, $breakpoints);\n $max: breakpoint-max($name, $breakpoints);\n\n @if $min != null and $max != null {\n @media (min-width: $min) and (max-width: $max) {\n @content;\n }\n } @else if $max == null {\n @include media-breakpoint-up($name, $breakpoints) {\n @content;\n }\n } @else if $min == null {\n @include media-breakpoint-down($name, $breakpoints) {\n @content;\n }\n }\n}\n", + "// Hover mixin and `$enable-hover-media-query` are deprecated.\n//\n// Originally added during our alphas and maintained during betas, this mixin was\n// designed to prevent `:hover` stickiness on iOS-an issue where hover styles\n// would persist after initial touch.\n//\n// For backward compatibility, we've kept these mixins and updated them to\n// always return their regular pseudo-classes instead of a shimmed media query.\n//\n// Issue: https://github.com/twbs/bootstrap/issues/25195\n\n@mixin hover() {\n &:hover { @content; }\n}\n\n@mixin hover-focus() {\n &:hover,\n &:focus {\n @content;\n }\n}\n\n@mixin plain-hover-focus() {\n &,\n &:hover,\n &:focus {\n @content;\n }\n}\n\n@mixin hover-focus-active() {\n &:hover,\n &:focus,\n &:active {\n @content;\n }\n}\n", + "// Image Mixins\n// - Responsive image\n// - Retina image\n\n\n// Responsive image\n//\n// Keep images from scaling beyond the width of their parents.\n\n@mixin img-fluid() {\n // Part 1: Set a maximum relative to the parent\n max-width: 100%;\n // Part 2: Override the height to auto, otherwise images will be stretched\n // when setting a width and height attribute on the img element.\n height: auto;\n}\n\n\n// Retina image\n//\n// Short retina mixin for setting background-image and -size.\n\n@mixin img-retina($file-1x, $file-2x, $width-1x, $height-1x) {\n background-image: url($file-1x);\n\n // Autoprefixer takes care of adding -webkit-min-device-pixel-ratio and -o-min-device-pixel-ratio,\n // but doesn't convert dppx=>dpi.\n // There's no such thing as unprefixed min-device-pixel-ratio since it's nonstandard.\n // Compatibility info: https://caniuse.com/#feat=css-media-resolution\n @media only screen and (min-resolution: 192dpi), // IE9-11 don't support dppx\n only screen and (min-resolution: 2dppx) { // Standardized\n background-image: url($file-2x);\n background-size: $width-1x $height-1x;\n }\n @include deprecate(\"`img-retina()`\", \"v4.3.0\", \"v5\");\n}\n", + "@mixin badge-variant($bg) {\n color: color-yiq($bg);\n background-color: $bg;\n\n @at-root a#{&} {\n @include hover-focus() {\n color: color-yiq($bg);\n background-color: darken($bg, 10%);\n }\n\n &:focus,\n &.focus {\n outline: 0;\n box-shadow: 0 0 0 $badge-focus-width rgba($bg, .5);\n }\n }\n}\n", + "// Resize anything\n\n@mixin resizable($direction) {\n overflow: auto; // Per CSS3 UI, `resize` only applies when `overflow` isn't `visible`\n resize: $direction; // Options: horizontal, vertical, both\n}\n", + "// Only display content to screen readers\n//\n// See: https://www.a11yproject.com/posts/2013-01-11-how-to-hide-content/\n// See: https://hugogiraudel.com/2016/10/13/css-hide-and-seek/\n\n@mixin sr-only() {\n position: absolute;\n width: 1px;\n height: 1px;\n padding: 0;\n margin: -1px; // Fix for https://github.com/twbs/bootstrap/issues/25686\n overflow: hidden;\n clip: rect(0, 0, 0, 0);\n white-space: nowrap;\n border: 0;\n}\n\n// Use in conjunction with .sr-only to only display content when it's focused.\n//\n// Useful for \"Skip to main content\" links; see https://www.w3.org/TR/2013/NOTE-WCAG20-TECHS-20130905/G1\n//\n// Credit: HTML5 Boilerplate\n\n@mixin sr-only-focusable() {\n &:active,\n &:focus {\n position: static;\n width: auto;\n height: auto;\n overflow: visible;\n clip: auto;\n white-space: normal;\n }\n}\n", + "// Sizing shortcuts\n\n@mixin size($width, $height: $width) {\n width: $width;\n height: $height;\n @include deprecate(\"`size()`\", \"v4.3.0\", \"v5\");\n}\n", + "@mixin reset-text() {\n font-family: $font-family-base;\n // We deliberately do NOT reset font-size or word-wrap.\n font-style: normal;\n font-weight: $font-weight-normal;\n line-height: $line-height-base;\n text-align: left; // Fallback for where `start` is not supported\n text-align: start;\n text-decoration: none;\n text-shadow: none;\n text-transform: none;\n letter-spacing: normal;\n word-break: normal;\n word-spacing: normal;\n white-space: normal;\n line-break: auto;\n}\n", + "// stylelint-disable declaration-no-important\n\n// Typography\n\n@mixin text-emphasis-variant($parent, $color, $ignore-warning: false) {\n #{$parent} {\n color: $color !important;\n }\n @if $emphasized-link-hover-darken-percentage != 0 {\n a#{$parent} {\n @include hover-focus() {\n color: darken($color, $emphasized-link-hover-darken-percentage) !important;\n }\n }\n }\n @include deprecate(\"`text-emphasis-variant()`\", \"v4.4.0\", \"v5\", $ignore-warning);\n}\n", + "// CSS image replacement\n@mixin text-hide($ignore-warning: false) {\n // stylelint-disable-next-line font-family-no-missing-generic-family-keyword\n font: 0/0 a;\n color: transparent;\n text-shadow: none;\n background-color: transparent;\n border: 0;\n\n @include deprecate(\"`text-hide()`\", \"v4.1.0\", \"v5\", $ignore-warning);\n}\n", + "// Text truncate\n// Requires inline-block or block for proper styling\n\n@mixin text-truncate() {\n overflow: hidden;\n text-overflow: ellipsis;\n white-space: nowrap;\n}\n", + "// stylelint-disable declaration-no-important\n\n// Visibility\n\n@mixin invisible($visibility) {\n visibility: $visibility !important;\n @include deprecate(\"`invisible()`\", \"v4.3.0\", \"v5\");\n}\n", + "@mixin alert-variant($background, $border, $color) {\n color: $color;\n @include gradient-bg($background);\n border-color: $border;\n\n hr {\n border-top-color: darken($border, 5%);\n }\n\n .alert-link {\n color: darken($color, 10%);\n }\n}\n", + "// Button variants\n//\n// Easily pump out default styles, as well as :hover, :focus, :active,\n// and disabled options for all buttons\n\n@mixin button-variant($background, $border, $hover-background: darken($background, 7.5%), $hover-border: darken($border, 10%), $active-background: darken($background, 10%), $active-border: darken($border, 12.5%)) {\n color: color-yiq($background);\n @include gradient-bg($background);\n border-color: $border;\n @include box-shadow($btn-box-shadow);\n\n @include hover() {\n color: color-yiq($hover-background);\n @include gradient-bg($hover-background);\n border-color: $hover-border;\n }\n\n &:focus,\n &.focus {\n color: color-yiq($hover-background);\n @include gradient-bg($hover-background);\n border-color: $hover-border;\n @if $enable-shadows {\n @include box-shadow($btn-box-shadow, 0 0 0 $btn-focus-width rgba(mix(color-yiq($background), $border, 15%), .5));\n } @else {\n // Avoid using mixin so we can pass custom focus shadow properly\n box-shadow: 0 0 0 $btn-focus-width rgba(mix(color-yiq($background), $border, 15%), .5);\n }\n }\n\n // Disabled comes first so active can properly restyle\n &.disabled,\n &:disabled {\n color: color-yiq($background);\n background-color: $background;\n border-color: $border;\n // Remove CSS gradients if they're enabled\n @if $enable-gradients {\n background-image: none;\n }\n }\n\n &:not(:disabled):not(.disabled):active,\n &:not(:disabled):not(.disabled).active,\n .show > &.dropdown-toggle {\n color: color-yiq($active-background);\n background-color: $active-background;\n @if $enable-gradients {\n background-image: none; // Remove the gradient for the pressed/active state\n }\n border-color: $active-border;\n\n &:focus {\n @if $enable-shadows and $btn-active-box-shadow != none {\n @include box-shadow($btn-active-box-shadow, 0 0 0 $btn-focus-width rgba(mix(color-yiq($background), $border, 15%), .5));\n } @else {\n // Avoid using mixin so we can pass custom focus shadow properly\n box-shadow: 0 0 0 $btn-focus-width rgba(mix(color-yiq($background), $border, 15%), .5);\n }\n }\n }\n}\n\n@mixin button-outline-variant($color, $color-hover: color-yiq($color), $active-background: $color, $active-border: $color) {\n color: $color;\n border-color: $color;\n\n @include hover() {\n color: $color-hover;\n background-color: $active-background;\n border-color: $active-border;\n }\n\n &:focus,\n &.focus {\n box-shadow: 0 0 0 $btn-focus-width rgba($color, .5);\n }\n\n &.disabled,\n &:disabled {\n color: $color;\n background-color: transparent;\n }\n\n &:not(:disabled):not(.disabled):active,\n &:not(:disabled):not(.disabled).active,\n .show > &.dropdown-toggle {\n color: color-yiq($active-background);\n background-color: $active-background;\n border-color: $active-border;\n\n &:focus {\n @if $enable-shadows and $btn-active-box-shadow != none {\n @include box-shadow($btn-active-box-shadow, 0 0 0 $btn-focus-width rgba($color, .5));\n } @else {\n // Avoid using mixin so we can pass custom focus shadow properly\n box-shadow: 0 0 0 $btn-focus-width rgba($color, .5);\n }\n }\n }\n}\n\n// Button sizes\n@mixin button-size($padding-y, $padding-x, $font-size, $line-height, $border-radius) {\n padding: $padding-y $padding-x;\n @include font-size($font-size);\n line-height: $line-height;\n // Manually declare to provide an override to the browser default\n @include border-radius($border-radius, 0);\n}\n", + "@mixin caret-down() {\n border-top: $caret-width solid;\n border-right: $caret-width solid transparent;\n border-bottom: 0;\n border-left: $caret-width solid transparent;\n}\n\n@mixin caret-up() {\n border-top: 0;\n border-right: $caret-width solid transparent;\n border-bottom: $caret-width solid;\n border-left: $caret-width solid transparent;\n}\n\n@mixin caret-right() {\n border-top: $caret-width solid transparent;\n border-right: 0;\n border-bottom: $caret-width solid transparent;\n border-left: $caret-width solid;\n}\n\n@mixin caret-left() {\n border-top: $caret-width solid transparent;\n border-right: $caret-width solid;\n border-bottom: $caret-width solid transparent;\n}\n\n@mixin caret($direction: down) {\n @if $enable-caret {\n &::after {\n display: inline-block;\n margin-left: $caret-spacing;\n vertical-align: $caret-vertical-align;\n content: \"\";\n @if $direction == down {\n @include caret-down();\n } @else if $direction == up {\n @include caret-up();\n } @else if $direction == right {\n @include caret-right();\n }\n }\n\n @if $direction == left {\n &::after {\n display: none;\n }\n\n &::before {\n display: inline-block;\n margin-right: $caret-spacing;\n vertical-align: $caret-vertical-align;\n content: \"\";\n @include caret-left();\n }\n }\n\n &:empty::after {\n margin-left: 0;\n }\n }\n}\n", + "// Pagination\n\n@mixin pagination-size($padding-y, $padding-x, $font-size, $line-height, $border-radius) {\n .page-link {\n padding: $padding-y $padding-x;\n @include font-size($font-size);\n line-height: $line-height;\n }\n\n .page-item {\n &:first-child {\n .page-link {\n @include border-left-radius($border-radius);\n }\n }\n &:last-child {\n .page-link {\n @include border-right-radius($border-radius);\n }\n }\n }\n}\n", + "// Lists\n\n// Unstyled keeps list items block level, just removes default browser padding and list-style\n@mixin list-unstyled() {\n padding-left: 0;\n list-style: none;\n}\n", + "// List Groups\n\n@mixin list-group-item-variant($state, $background, $color) {\n .list-group-item-#{$state} {\n color: $color;\n background-color: $background;\n\n &.list-group-item-action {\n @include hover-focus() {\n color: $color;\n background-color: darken($background, 5%);\n }\n\n &.active {\n color: $white;\n background-color: $color;\n border-color: $color;\n }\n }\n }\n}\n", + "// Horizontal dividers\n//\n// Dividers (basically an hr) within dropdowns and nav lists\n\n@mixin nav-divider($color: $nav-divider-color, $margin-y: $nav-divider-margin-y, $ignore-warning: false) {\n height: 0;\n margin: $margin-y 0;\n overflow: hidden;\n border-top: 1px solid $color;\n @include deprecate(\"The `nav-divider()` mixin\", \"v4.4.0\", \"v5\", $ignore-warning);\n}\n", + "// Form control focus state\n//\n// Generate a customized focus state and for any input with the specified color,\n// which defaults to the `$input-focus-border-color` variable.\n//\n// We highly encourage you to not customize the default value, but instead use\n// this to tweak colors on an as-needed basis. This aesthetic change is based on\n// WebKit's default styles, but applicable to a wider range of browsers. Its\n// usability and accessibility should be taken into account with any change.\n//\n// Example usage: change the default blue border and shadow to white for better\n// contrast against a dark gray background.\n@mixin form-control-focus($ignore-warning: false) {\n &:focus {\n color: $input-focus-color;\n background-color: $input-focus-bg;\n border-color: $input-focus-border-color;\n outline: 0;\n @if $enable-shadows {\n @include box-shadow($input-box-shadow, $input-focus-box-shadow);\n } @else {\n // Avoid using mixin so we can pass custom focus shadow properly\n box-shadow: $input-focus-box-shadow;\n }\n }\n @include deprecate(\"The `form-control-focus()` mixin\", \"v4.4.0\", \"v5\", $ignore-warning);\n}\n\n// This mixin uses an `if()` technique to be compatible with Dart Sass\n// See https://github.com/sass/sass/issues/1873#issuecomment-152293725 for more details\n@mixin form-validation-state-selector($state) {\n @if ($state == \"valid\" or $state == \"invalid\") {\n .was-validated #{if(&, \"&\", \"\")}:#{$state},\n #{if(&, \"&\", \"\")}.is-#{$state} {\n @content;\n }\n } @else {\n #{if(&, \"&\", \"\")}.is-#{$state} {\n @content;\n }\n }\n}\n\n@mixin form-validation-state($state, $color, $icon) {\n .#{$state}-feedback {\n display: none;\n width: 100%;\n margin-top: $form-feedback-margin-top;\n @include font-size($form-feedback-font-size);\n color: $color;\n }\n\n .#{$state}-tooltip {\n position: absolute;\n top: 100%;\n left: 0;\n z-index: 5;\n display: none;\n max-width: 100%; // Contain to parent when possible\n padding: $form-feedback-tooltip-padding-y $form-feedback-tooltip-padding-x;\n margin-top: .1rem;\n @include font-size($form-feedback-tooltip-font-size);\n line-height: $form-feedback-tooltip-line-height;\n color: color-yiq($color);\n background-color: rgba($color, $form-feedback-tooltip-opacity);\n @include border-radius($form-feedback-tooltip-border-radius);\n }\n\n @include form-validation-state-selector($state) {\n ~ .#{$state}-feedback,\n ~ .#{$state}-tooltip {\n display: block;\n }\n }\n\n .form-control {\n @include form-validation-state-selector($state) {\n border-color: $color;\n\n @if $enable-validation-icons {\n padding-right: $input-height-inner;\n background-image: escape-svg($icon);\n background-repeat: no-repeat;\n background-position: right $input-height-inner-quarter center;\n background-size: $input-height-inner-half $input-height-inner-half;\n }\n\n &:focus {\n border-color: $color;\n box-shadow: 0 0 0 $input-focus-width rgba($color, .25);\n }\n }\n }\n\n // stylelint-disable-next-line selector-no-qualifying-type\n textarea.form-control {\n @include form-validation-state-selector($state) {\n @if $enable-validation-icons {\n padding-right: $input-height-inner;\n background-position: top $input-height-inner-quarter right $input-height-inner-quarter;\n }\n }\n }\n\n .custom-select {\n @include form-validation-state-selector($state) {\n border-color: $color;\n\n @if $enable-validation-icons {\n padding-right: $custom-select-feedback-icon-padding-right;\n background: $custom-select-background, escape-svg($icon) $custom-select-bg no-repeat $custom-select-feedback-icon-position / $custom-select-feedback-icon-size;\n }\n\n &:focus {\n border-color: $color;\n box-shadow: 0 0 0 $input-focus-width rgba($color, .25);\n }\n }\n }\n\n .form-check-input {\n @include form-validation-state-selector($state) {\n ~ .form-check-label {\n color: $color;\n }\n\n ~ .#{$state}-feedback,\n ~ .#{$state}-tooltip {\n display: block;\n }\n }\n }\n\n .custom-control-input {\n @include form-validation-state-selector($state) {\n ~ .custom-control-label {\n color: $color;\n\n &::before {\n border-color: $color;\n }\n }\n\n &:checked {\n ~ .custom-control-label::before {\n border-color: lighten($color, 10%);\n @include gradient-bg(lighten($color, 10%));\n }\n }\n\n &:focus {\n ~ .custom-control-label::before {\n box-shadow: 0 0 0 $input-focus-width rgba($color, .25);\n }\n\n &:not(:checked) ~ .custom-control-label::before {\n border-color: $color;\n }\n }\n }\n }\n\n // custom file\n .custom-file-input {\n @include form-validation-state-selector($state) {\n ~ .custom-file-label {\n border-color: $color;\n }\n\n &:focus {\n ~ .custom-file-label {\n border-color: $color;\n box-shadow: 0 0 0 $input-focus-width rgba($color, .25);\n }\n }\n }\n }\n}\n", + "// Tables\n\n@mixin table-row-variant($state, $background, $border: null) {\n // Exact selectors below required to override `.table-striped` and prevent\n // inheritance to nested tables.\n .table-#{$state} {\n &,\n > th,\n > td {\n background-color: $background;\n }\n\n @if $border != null {\n th,\n td,\n thead th,\n tbody + tbody {\n border-color: $border;\n }\n }\n }\n\n // Hover states for `.table-hover`\n // Note: this is not available for cells or rows within `thead` or `tfoot`.\n .table-hover {\n $hover-background: darken($background, 5%);\n\n .table-#{$state} {\n @include hover() {\n background-color: $hover-background;\n\n > td,\n > th {\n background-color: $hover-background;\n }\n }\n }\n }\n}\n", + "// stylelint-disable declaration-no-important\n\n// Contextual backgrounds\n\n@mixin bg-variant($parent, $color, $ignore-warning: false) {\n #{$parent} {\n background-color: $color !important;\n }\n a#{$parent},\n button#{$parent} {\n @include hover-focus() {\n background-color: darken($color, 10%) !important;\n }\n }\n @include deprecate(\"The `bg-variant` mixin\", \"v4.4.0\", \"v5\", $ignore-warning);\n}\n\n@mixin bg-gradient-variant($parent, $color, $ignore-warning: false) {\n #{$parent} {\n background: $color linear-gradient(180deg, mix($body-bg, $color, 15%), $color) repeat-x !important;\n }\n @include deprecate(\"The `bg-gradient-variant` mixin\", \"v4.5.0\", \"v5\", $ignore-warning);\n}\n", + "// stylelint-disable property-disallowed-list\n// Single side border-radius\n\n// Helper function to replace negative values with 0\n@function valid-radius($radius) {\n $return: ();\n @each $value in $radius {\n @if type-of($value) == number {\n $return: append($return, max($value, 0));\n } @else {\n $return: append($return, $value);\n }\n }\n @return $return;\n}\n\n@mixin border-radius($radius: $border-radius, $fallback-border-radius: false) {\n @if $enable-rounded {\n border-radius: valid-radius($radius);\n }\n @else if $fallback-border-radius != false {\n border-radius: $fallback-border-radius;\n }\n}\n\n@mixin border-top-radius($radius) {\n @if $enable-rounded {\n border-top-left-radius: valid-radius($radius);\n border-top-right-radius: valid-radius($radius);\n }\n}\n\n@mixin border-right-radius($radius) {\n @if $enable-rounded {\n border-top-right-radius: valid-radius($radius);\n border-bottom-right-radius: valid-radius($radius);\n }\n}\n\n@mixin border-bottom-radius($radius) {\n @if $enable-rounded {\n border-bottom-right-radius: valid-radius($radius);\n border-bottom-left-radius: valid-radius($radius);\n }\n}\n\n@mixin border-left-radius($radius) {\n @if $enable-rounded {\n border-top-left-radius: valid-radius($radius);\n border-bottom-left-radius: valid-radius($radius);\n }\n}\n\n@mixin border-top-left-radius($radius) {\n @if $enable-rounded {\n border-top-left-radius: valid-radius($radius);\n }\n}\n\n@mixin border-top-right-radius($radius) {\n @if $enable-rounded {\n border-top-right-radius: valid-radius($radius);\n }\n}\n\n@mixin border-bottom-right-radius($radius) {\n @if $enable-rounded {\n border-bottom-right-radius: valid-radius($radius);\n }\n}\n\n@mixin border-bottom-left-radius($radius) {\n @if $enable-rounded {\n border-bottom-left-radius: valid-radius($radius);\n }\n}\n", + "@mixin box-shadow($shadow...) {\n @if $enable-shadows {\n $result: ();\n\n @if (length($shadow) == 1) {\n // We can pass `@include box-shadow(none);`\n $result: $shadow;\n } @else {\n // Filter to avoid invalid properties for example `box-shadow: none, 1px 1px black;`\n @for $i from 1 through length($shadow) {\n @if nth($shadow, $i) != \"none\" {\n $result: append($result, nth($shadow, $i), \"comma\");\n }\n }\n }\n @if (length($result) > 0) {\n box-shadow: $result;\n }\n }\n}\n", + "// Gradients\n\n@mixin gradient-bg($color) {\n @if $enable-gradients {\n background: $color linear-gradient(180deg, mix($body-bg, $color, 15%), $color) repeat-x;\n } @else {\n background-color: $color;\n }\n}\n\n// Horizontal gradient, from left to right\n//\n// Creates two color stops, start and end, by specifying a color and position for each color stop.\n@mixin gradient-x($start-color: $gray-700, $end-color: $gray-800, $start-percent: 0%, $end-percent: 100%) {\n background-image: linear-gradient(to right, $start-color $start-percent, $end-color $end-percent);\n background-repeat: repeat-x;\n}\n\n// Vertical gradient, from top to bottom\n//\n// Creates two color stops, start and end, by specifying a color and position for each color stop.\n@mixin gradient-y($start-color: $gray-700, $end-color: $gray-800, $start-percent: 0%, $end-percent: 100%) {\n background-image: linear-gradient(to bottom, $start-color $start-percent, $end-color $end-percent);\n background-repeat: repeat-x;\n}\n\n@mixin gradient-directional($start-color: $gray-700, $end-color: $gray-800, $deg: 45deg) {\n background-image: linear-gradient($deg, $start-color, $end-color);\n background-repeat: repeat-x;\n}\n@mixin gradient-x-three-colors($start-color: $blue, $mid-color: $purple, $color-stop: 50%, $end-color: $red) {\n background-image: linear-gradient(to right, $start-color, $mid-color $color-stop, $end-color);\n background-repeat: no-repeat;\n}\n@mixin gradient-y-three-colors($start-color: $blue, $mid-color: $purple, $color-stop: 50%, $end-color: $red) {\n background-image: linear-gradient($start-color, $mid-color $color-stop, $end-color);\n background-repeat: no-repeat;\n}\n@mixin gradient-radial($inner-color: $gray-700, $outer-color: $gray-800) {\n background-image: radial-gradient(circle, $inner-color, $outer-color);\n background-repeat: no-repeat;\n}\n@mixin gradient-striped($color: rgba($white, .15), $angle: 45deg) {\n background-image: linear-gradient($angle, $color 25%, transparent 25%, transparent 50%, $color 50%, $color 75%, transparent 75%, transparent);\n}\n", + "// stylelint-disable property-disallowed-list\n@mixin transition($transition...) {\n @if length($transition) == 0 {\n $transition: $transition-base;\n }\n\n @if length($transition) > 1 {\n @each $value in $transition {\n @if $value == null or $value == none {\n @warn \"The keyword 'none' or 'null' must be used as a single argument.\";\n }\n }\n }\n\n @if $enable-transitions {\n @if nth($transition, 1) != null {\n transition: $transition;\n }\n\n @if $enable-prefers-reduced-motion-media-query and nth($transition, 1) != null and nth($transition, 1) != none {\n @media (prefers-reduced-motion: reduce) {\n transition: none;\n }\n }\n }\n}\n", + "@mixin clearfix() {\n &::after {\n display: block;\n clear: both;\n content: \"\";\n }\n}\n", + "// Framework grid generation\n//\n// Used only by Bootstrap to generate the correct number of grid classes given\n// any value of `$grid-columns`.\n\n@mixin make-grid-columns($columns: $grid-columns, $gutter: $grid-gutter-width, $breakpoints: $grid-breakpoints) {\n // Common properties for all breakpoints\n %grid-column {\n position: relative;\n width: 100%;\n padding-right: $gutter / 2;\n padding-left: $gutter / 2;\n }\n\n @each $breakpoint in map-keys($breakpoints) {\n $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n @if $columns > 0 {\n // Allow columns to stretch full width below their breakpoints\n @for $i from 1 through $columns {\n .col#{$infix}-#{$i} {\n @extend %grid-column;\n }\n }\n }\n\n .col#{$infix},\n .col#{$infix}-auto {\n @extend %grid-column;\n }\n\n @include media-breakpoint-up($breakpoint, $breakpoints) {\n // Provide basic `.col-{bp}` classes for equal-width flexbox columns\n .col#{$infix} {\n flex-basis: 0;\n flex-grow: 1;\n max-width: 100%;\n }\n\n @if $grid-row-columns > 0 {\n @for $i from 1 through $grid-row-columns {\n .row-cols#{$infix}-#{$i} {\n @include row-cols($i);\n }\n }\n }\n\n .col#{$infix}-auto {\n @include make-col-auto();\n }\n\n @if $columns > 0 {\n @for $i from 1 through $columns {\n .col#{$infix}-#{$i} {\n @include make-col($i, $columns);\n }\n }\n }\n\n .order#{$infix}-first { order: -1; }\n\n .order#{$infix}-last { order: $columns + 1; }\n\n @for $i from 0 through $columns {\n .order#{$infix}-#{$i} { order: $i; }\n }\n\n @if $columns > 0 {\n // `$columns - 1` because offsetting by the width of an entire row isn't possible\n @for $i from 0 through ($columns - 1) {\n @if not ($infix == \"\" and $i == 0) { // Avoid emitting useless .offset-0\n .offset#{$infix}-#{$i} {\n @include make-col-offset($i, $columns);\n }\n }\n }\n }\n }\n }\n}\n", + "/// Grid system\n//\n// Generate semantic grid columns with these mixins.\n\n@mixin make-container($gutter: $grid-gutter-width) {\n width: 100%;\n padding-right: $gutter / 2;\n padding-left: $gutter / 2;\n margin-right: auto;\n margin-left: auto;\n}\n\n@mixin make-row($gutter: $grid-gutter-width) {\n display: flex;\n flex-wrap: wrap;\n margin-right: -$gutter / 2;\n margin-left: -$gutter / 2;\n}\n\n// For each breakpoint, define the maximum width of the container in a media query\n@mixin make-container-max-widths($max-widths: $container-max-widths, $breakpoints: $grid-breakpoints) {\n @each $breakpoint, $container-max-width in $max-widths {\n @include media-breakpoint-up($breakpoint, $breakpoints) {\n max-width: $container-max-width;\n }\n }\n @include deprecate(\"The `make-container-max-widths` mixin\", \"v4.5.2\", \"v5\");\n}\n\n@mixin make-col-ready($gutter: $grid-gutter-width) {\n position: relative;\n // Prevent columns from becoming too narrow when at smaller grid tiers by\n // always setting `width: 100%;`. This works because we use `flex` values\n // later on to override this initial width.\n width: 100%;\n padding-right: $gutter / 2;\n padding-left: $gutter / 2;\n}\n\n@mixin make-col($size, $columns: $grid-columns) {\n flex: 0 0 percentage($size / $columns);\n // Add a `max-width` to ensure content within each column does not blow out\n // the width of the column. Applies to IE10+ and Firefox. Chrome and Safari\n // do not appear to require this.\n max-width: percentage($size / $columns);\n}\n\n@mixin make-col-auto() {\n flex: 0 0 auto;\n width: auto;\n max-width: 100%; // Reset earlier grid tiers\n}\n\n@mixin make-col-offset($size, $columns: $grid-columns) {\n $num: $size / $columns;\n margin-left: if($num == 0, 0, percentage($num));\n}\n\n// Row columns\n//\n// Specify on a parent element(e.g., .row) to force immediate children into NN\n// numberof columns. Supports wrapping to new lines, but does not do a Masonry\n// style grid.\n@mixin row-cols($count) {\n > * {\n flex: 0 0 100% / $count;\n max-width: 100% / $count;\n }\n}\n", + "// stylelint-disable declaration-no-important\n\n@mixin float-left() {\n float: left !important;\n @include deprecate(\"The `float-left` mixin\", \"v4.3.0\", \"v5\");\n}\n@mixin float-right() {\n float: right !important;\n @include deprecate(\"The `float-right` mixin\", \"v4.3.0\", \"v5\");\n}\n@mixin float-none() {\n float: none !important;\n @include deprecate(\"The `float-none` mixin\", \"v4.3.0\", \"v5\");\n}\n", + "// Do not forget to update getting-started/theming.md!\n:root {\n // Custom variable values only support SassScript inside `#{}`.\n @each $color, $value in $colors {\n --#{$color}: #{$value};\n }\n\n @each $color, $value in $theme-colors {\n --#{$color}: #{$value};\n }\n\n @each $bp, $value in $grid-breakpoints {\n --breakpoint-#{$bp}: #{$value};\n }\n\n // Use `inspect` for lists so that quoted items keep the quotes.\n // See https://github.com/sass/sass/issues/2383#issuecomment-336349172\n --font-family-sans-serif: #{inspect($font-family-sans-serif)};\n --font-family-monospace: #{inspect($font-family-monospace)};\n}\n", + "// stylelint-disable at-rule-no-vendor-prefix, declaration-no-important, selector-no-qualifying-type, property-no-vendor-prefix\n\n// Reboot\n//\n// Normalization of HTML elements, manually forked from Normalize.css to remove\n// styles targeting irrelevant browsers while applying new styles.\n//\n// Normalize is licensed MIT. https://github.com/necolas/normalize.css\n\n\n// Document\n//\n// 1. Change from `box-sizing: content-box` so that `width` is not affected by `padding` or `border`.\n// 2. Change the default font family in all browsers.\n// 3. Correct the line height in all browsers.\n// 4. Prevent adjustments of font size after orientation changes in IE on Windows Phone and in iOS.\n// 5. Change the default tap highlight to be completely transparent in iOS.\n\n*,\n*::before,\n*::after {\n box-sizing: border-box; // 1\n}\n\nhtml {\n font-family: sans-serif; // 2\n line-height: 1.15; // 3\n -webkit-text-size-adjust: 100%; // 4\n -webkit-tap-highlight-color: rgba($black, 0); // 5\n}\n\n// Shim for \"new\" HTML5 structural elements to display correctly (IE10, older browsers)\n// TODO: remove in v5\n// stylelint-disable-next-line selector-list-comma-newline-after\narticle, aside, figcaption, figure, footer, header, hgroup, main, nav, section {\n display: block;\n}\n\n// Body\n//\n// 1. Remove the margin in all browsers.\n// 2. As a best practice, apply a default `background-color`.\n// 3. Set an explicit initial text-align value so that we can later use\n// the `inherit` value on things like `` elements.\n\nbody {\n margin: 0; // 1\n font-family: $font-family-base;\n @include font-size($font-size-base);\n font-weight: $font-weight-base;\n line-height: $line-height-base;\n color: $body-color;\n text-align: left; // 3\n background-color: $body-bg; // 2\n}\n\n// Future-proof rule: in browsers that support :focus-visible, suppress the focus outline\n// on elements that programmatically receive focus but wouldn't normally show a visible\n// focus outline. In general, this would mean that the outline is only applied if the\n// interaction that led to the element receiving programmatic focus was a keyboard interaction,\n// or the browser has somehow determined that the user is primarily a keyboard user and/or\n// wants focus outlines to always be presented.\n//\n// See https://developer.mozilla.org/en-US/docs/Web/CSS/:focus-visible\n// and https://developer.paciellogroup.com/blog/2018/03/focus-visible-and-backwards-compatibility/\n[tabindex=\"-1\"]:focus:not(:focus-visible) {\n outline: 0 !important;\n}\n\n\n// Content grouping\n//\n// 1. Add the correct box sizing in Firefox.\n// 2. Show the overflow in Edge and IE.\n\nhr {\n box-sizing: content-box; // 1\n height: 0; // 1\n overflow: visible; // 2\n}\n\n\n//\n// Typography\n//\n\n// Remove top margins from headings\n//\n// By default, `

`-`

` all receive top and bottom margins. We nuke the top\n// margin for easier control within type scales as it avoids margin collapsing.\n// stylelint-disable-next-line selector-list-comma-newline-after\nh1, h2, h3, h4, h5, h6 {\n margin-top: 0;\n margin-bottom: $headings-margin-bottom;\n}\n\n// Reset margins on paragraphs\n//\n// Similarly, the top margin on `

`s get reset. However, we also reset the\n// bottom margin to use `rem` units instead of `em`.\np {\n margin-top: 0;\n margin-bottom: $paragraph-margin-bottom;\n}\n\n// Abbreviations\n//\n// 1. Duplicate behavior to the data-* attribute for our tooltip plugin\n// 2. Add the correct text decoration in Chrome, Edge, IE, Opera, and Safari.\n// 3. Add explicit cursor to indicate changed behavior.\n// 4. Remove the bottom border in Firefox 39-.\n// 5. Prevent the text-decoration to be skipped.\n\nabbr[title],\nabbr[data-original-title] { // 1\n text-decoration: underline; // 2\n text-decoration: underline dotted; // 2\n cursor: help; // 3\n border-bottom: 0; // 4\n text-decoration-skip-ink: none; // 5\n}\n\naddress {\n margin-bottom: 1rem;\n font-style: normal;\n line-height: inherit;\n}\n\nol,\nul,\ndl {\n margin-top: 0;\n margin-bottom: 1rem;\n}\n\nol ol,\nul ul,\nol ul,\nul ol {\n margin-bottom: 0;\n}\n\ndt {\n font-weight: $dt-font-weight;\n}\n\ndd {\n margin-bottom: .5rem;\n margin-left: 0; // Undo browser default\n}\n\nblockquote {\n margin: 0 0 1rem;\n}\n\nb,\nstrong {\n font-weight: $font-weight-bolder; // Add the correct font weight in Chrome, Edge, and Safari\n}\n\nsmall {\n @include font-size(80%); // Add the correct font size in all browsers\n}\n\n//\n// Prevent `sub` and `sup` elements from affecting the line height in\n// all browsers.\n//\n\nsub,\nsup {\n position: relative;\n @include font-size(75%);\n line-height: 0;\n vertical-align: baseline;\n}\n\nsub { bottom: -.25em; }\nsup { top: -.5em; }\n\n\n//\n// Links\n//\n\na {\n color: $link-color;\n text-decoration: $link-decoration;\n background-color: transparent; // Remove the gray background on active links in IE 10.\n\n @include hover() {\n color: $link-hover-color;\n text-decoration: $link-hover-decoration;\n }\n}\n\n// And undo these styles for placeholder links/named anchors (without href).\n// It would be more straightforward to just use a[href] in previous block, but that\n// causes specificity issues in many other styles that are too complex to fix.\n// See https://github.com/twbs/bootstrap/issues/19402\n\na:not([href]):not([class]) {\n color: inherit;\n text-decoration: none;\n\n @include hover() {\n color: inherit;\n text-decoration: none;\n }\n}\n\n\n//\n// Code\n//\n\npre,\ncode,\nkbd,\nsamp {\n font-family: $font-family-monospace;\n @include font-size(1em); // Correct the odd `em` font sizing in all browsers.\n}\n\npre {\n // Remove browser default top margin\n margin-top: 0;\n // Reset browser default of `1em` to use `rem`s\n margin-bottom: 1rem;\n // Don't allow content to break outside\n overflow: auto;\n // Disable auto-hiding scrollbar in IE & legacy Edge to avoid overlap,\n // making it impossible to interact with the content\n -ms-overflow-style: scrollbar;\n}\n\n\n//\n// Figures\n//\n\nfigure {\n // Apply a consistent margin strategy (matches our type styles).\n margin: 0 0 1rem;\n}\n\n\n//\n// Images and content\n//\n\nimg {\n vertical-align: middle;\n border-style: none; // Remove the border on images inside links in IE 10-.\n}\n\nsvg {\n // Workaround for the SVG overflow bug in IE10/11 is still required.\n // See https://github.com/twbs/bootstrap/issues/26878\n overflow: hidden;\n vertical-align: middle;\n}\n\n\n//\n// Tables\n//\n\ntable {\n border-collapse: collapse; // Prevent double borders\n}\n\ncaption {\n padding-top: $table-cell-padding;\n padding-bottom: $table-cell-padding;\n color: $table-caption-color;\n text-align: left;\n caption-side: bottom;\n}\n\n// 1. Removes font-weight bold by inheriting\n// 2. Matches default `` alignment by inheriting `text-align`.\n// 3. Fix alignment for Safari\n\nth {\n font-weight: $table-th-font-weight; // 1\n text-align: inherit; // 2\n text-align: -webkit-match-parent; // 3\n}\n\n\n//\n// Forms\n//\n\nlabel {\n // Allow labels to use `margin` for spacing.\n display: inline-block;\n margin-bottom: $label-margin-bottom;\n}\n\n// Remove the default `border-radius` that macOS Chrome adds.\n//\n// Details at https://github.com/twbs/bootstrap/issues/24093\nbutton {\n // stylelint-disable-next-line property-disallowed-list\n border-radius: 0;\n}\n\n// Work around a Firefox/IE bug where the transparent `button` background\n// results in a loss of the default `button` focus styles.\n//\n// Credit: https://github.com/suitcss/base/\nbutton:focus {\n outline: 1px dotted;\n outline: 5px auto -webkit-focus-ring-color;\n}\n\ninput,\nbutton,\nselect,\noptgroup,\ntextarea {\n margin: 0; // Remove the margin in Firefox and Safari\n font-family: inherit;\n @include font-size(inherit);\n line-height: inherit;\n}\n\nbutton,\ninput {\n overflow: visible; // Show the overflow in Edge\n}\n\nbutton,\nselect {\n text-transform: none; // Remove the inheritance of text transform in Firefox\n}\n\n// Set the cursor for non-` + +

+ + + + +
+

+ RLEM Workshop 2023 +

+
+
+ + + + + Document + + + +
+ +

+ Call for Papers +

+ +

Buildings account for 40% of the global energy consumption and 30% of the associated greenhouse gas emissions, while also offering a 50–90% CO2 mitigation potential. The transportation sector is responsible for an additional 30%. Optimal decarbonization requires electrification of end-uses and concomitant decarbonization of electricity supply, efficient use of electricity for lighting, space heating, cooling and ventilation (HVAC), and domestic hot water generation, and upgrade of the thermal properties of buildings. A major driver for decarbonization are integration of renewable energy systems (RES) into the grid, and photovoltaics (PV) and solar-thermal collectors as well as thermal and electric storage into residential and commercial buildings. Electric vehicles (EVs), with their storage capacity and inherent connectivity, hold a great potential for integration with buildings.

+ +

The integration of these technologies must be done carefully to unlock their full potential. Artificial intelligence is regarded as a possible pathway to orchestrate these complexities of Smart Cities. In particular, (deep) reinforcement learning algorithms have seen an increased interest and have demonstrated human expert level performance in other domains, e.g., computer games. Research in the building and cities domain has been fragmented and with focus on different problems and using a variety of frameworks. The purpose of this Workshop is to build a growing community around this exciting topic, provide a platform for discussion for future research direction, and share common frameworks.

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Topics of Interest

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Topics of interest include, but are not limited to:

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  • Challenges and Opportunities for RL in Building and Cities
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  • Explorations of model vs model-free RL algorithms and hybrids
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  • Comparisons of RL algorithms to other control solutions, e.g., model-predictive control
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  • Frameworks and datasets for benchmarking algorithms
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  • Theoretical contributions to the RL field brought about by constraints/challenges in the buildings/cities domain
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  • Applications (demand response, HVAC control, occupant integration, traffic scheduling, EV/battery charging, DER integration)
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  • Predicting energy consumption for Energy Demand Site Management
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  • Renewable energy forecasting for Energy Demand Site Management
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Submission Instructions

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We solicit submissions in two categories: Original Research Paper and Journal Track. Please indicate the category when submitting the work.

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  1. Original Research Paper: Submitted papers must be unpublished and must not be currently under review for any other publication. Paper submissions must be at most 4 single-spaced US Letter (8.5"x11") pages, including figures, tables, and appendices (excluding references). All submissions must use the LaTeX (preferred) or Word styles found here https://www.acm.org/publications/proceedings-template. Authors must make a good faith effort to anonymize their submissions by (1) using the "anonymous" option for the class and (2) using "anonsuppress" section where appropriate. Papers that do not meet the size, formatting, and anonymization requirements will not be reviewed. Please note that ACM uses 9-pt fonts in all conference proceedings, and the style (both LaTeX and Word) implicitly define the font size to be 9-pt.
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  2. Journal Track: Authors of published journal articles relevant to the workshop audience are invited to present their work, expand their outreach, and engage in discussions with the community. The requirement for the journal track presentation is that it must have been published within the last 12 months prior to the conference (since Nov 2022). Please submit the title, abstract and the DOI link to your paper. Paper will be selected by the program committee based on how well they fit the workshop scope, the allocated time in the program and interest to the community.
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Submission Link

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+ + + + + + + + + + + + diff --git a/dates/index.html b/dates/index.html new file mode 100644 index 0000000..5384170 --- /dev/null +++ b/dates/index.html @@ -0,0 +1,255 @@ + + + + + + + + Important Dates - RLEM Workshop 2023 + + + + + + + + + + + + + + + + + + + + + + + +
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+ RLEM Workshop 2023 +

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+ Important Dates +

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Paper submission:

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September 11, 2023 (AOE)

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Notifications:

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September 15, 2023 (AOE)

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Camera ready:

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September 29, 2023 (AOE)

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Workshop date:

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November 12, 2023

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+ + + + + + + + diff --git a/editions/2020/index.html b/editions/2020/index.html new file mode 100644 index 0000000..54f3e8e --- /dev/null +++ b/editions/2020/index.html @@ -0,0 +1,434 @@ + + + + + + + + RLEM Workshop 2020 - RLEM Workshop 2023 + + + + + + + + + + + + + + + + + + + + + + + +
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+ RLEM Workshop 2023 +

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+ + + + + Document + + + +
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+ Previous Edition: RLEM Workshop 2020 +

+

First ACM SIGEnergy Workshop on Reinforcement Learning for Energy Management in Buildings & Cities (RLEM)

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About

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RLEM brings together researchers and industry practitioners for the advancement of (deep) reinforcement learning (RL) in the built environment as it is applied for managing energy in civil infrastructure systems (energy, water, transportation).

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RLEM’21 will be held in conjunction with ACM BuildSys’20. Following BuildSys’s directive, the conference will be held virtually on November 17th 2020.

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Registration

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Register now via BuildSys 2020.

+ +

Important Dates

+ + +

Call for Papers

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Buildings account for 40% of the global energy consumption and 30% ofthe associated greenhouse gas emissions, while also offering a 50–90% CO2 mitigation potential. The transportation sector is responsible for an additional 30%. Optimal decarbonization requires electrification of end-uses a nd concomitant decarbonization of electricity supply, efficient use of electricity for lighting, space heating, cooling and ventilation (HVAC), and domestic hot water generation, and upgrade of the thermal properties of buildings. A major driver for decarbonization are integration of renewable energy systems (RES) into the grid, and photovoltaics (PV) and solar-thermal collectors as well as thermal and electric storage into residential and commercial buildings. Electric vehicles (EVs), with their storage capacity and inherent connectivity, hold a great potential for integration with buildings.

+ +

The integration of these technologies must be done carefully to unlock their full potential. Artificial intelligence is regarded as a possible pathway to orchestrate these complexities of Smart Cities. In particular, (deep) reinforcement learning algorithms have seen an increased interest and have demonstrated human expert level performance in other domains, e.g., computer games. Research in the building and cities domain has been fragmented and with focus on different problems and using a variety of frameworks. The purpose of this Workshop is to build a growing community around this exciting topic, provide a platform for discussion for future research direction, and share common frameworks.

+ +

Topics of Interest

+

Topics of interest include, but are not limited to:

+ + +

Submission Instructions

+

Submitted papers must be unpublished and must not be currently under review for any other publication. Paper submissions must be at most 4 single-spaced US Letter (8.5”x11”) pages, including figures, tables, and appendices (excluding references). All submissions must use the LaTeX (preferred) or Word styles found here https://www.acm.org/publications/proceedings-template. Authors must make a good faith effort to anonymize their submissions by (1) using the “anonymous” option for the class and (2) using “anonsuppress” section where appropriate. Papers that do not meet the size, formatting, and anonymization requirements will not be reviewed. Please note that ACM uses 9-pt fonts in all conference proceedings, and the style (both LaTeX and Word) implicitly define the font size to be 9-pt.

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Program

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All times are in Greenwich Meridian Time (GMT).

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TimeSessionTitleSpeakerAbstract
12:00-12:20Opening remarksGeneral Chair and TPC Chairs-
12:20-13:00KeynoteIncorporating robust control guarantees within (deep) reinforcement learningZico Kolter (Associate Professor, Carnegie Mellon University): Dr Kolter is an Associate Professor in the Computer Science Department with the School of Computer Science at Carnegie Mellon University. In addition, he also serves as Chief Scientist of AI Research for the Bosch Center for AI (BCAI), working in the Pittsburgh Office. His research group focuses on machine learning, optimization, and control. Specifically, much of the research aims at making deep learning algorithms safer, more robust, and more explainable; to these ends, we have worked on methods for training provably robust deep learning systems, and including more complex “modules” (such as optimization solvers) within the loop of deep architectures. Further focus is on several application domains, with a particular focus on applications in smart energy and sustainability domains.</span>Reinforcement learning methods have produced breakthrough results in recent years, but their application to safety-critical systems has been substantially limited by their lack of guarantees, such as those provided by modern robust control techniques. In this talk, I will discuss a technique we have recently developed that embeds robustness guarantees inside of arbitrary RL policy classes. Using this approach, we can build deep RL methods that attain much of the performance advantages of modern deep RL (namely, superior performance in "average case" scenarios), while still maintaining robustness in worst-case adversarial settings. I will highlight experimental results on several simple control systems highlighting the benefits of the method, in addition to a larger-scale smart grid setting, and end by discussing future directions in this line of work.
13:00-13:50Session 1: Demanding ResponseDemand Response through Price-setting Multi-agent Reinforcement LearningM.H. Christensen, C. Ernewein, P. Pinson (Technical University of Denmark)Price based demand response is a cost-effective way of obtaining flexibility needed in power systems with high penetration of intermittent renewable energy sources. Model-free deep reinforcement learning is proposed as a way to train autonomous agents for enabling buildings to participate in demand response programs as well as coordinating such programs though price setting in a multiagent setup. First, we show price responsive control of buildings with electric heat pumps using deep deterministic policy gradient. Then a coordinating agent is trained to manage a population of buildings by adjusting the price in order to keep the total load from exceeding the available capacity considering also the non-flexible base load.
Electricity Pricing aware Deep Reinforcement Learning based Intelligent HVAC ControlK. Kurte, J. Munk, K. Amasyali, O. Kotevska, R. Smith, H. Zandi (Oak Ridge National Laboratory)Recently, deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) has gained a lot of attention due to DRL's ability to optimally control HVAC for minimizing operational cost while maintaining resident's comfort. The success of such DRL-based techniques largely depends on the articulation of the problem in terms of states, actions, and reward function. Inclusion of the electricity pricing information in the problem formulation can play an important role in saving the cost of HVAC operation. However, less attention has been given in the literature on formulating well-crafted state features based on electricity pricing. In this work, we propose an approach for training the DRL model with a specific focus on feature engineering based on electricity pricing. During training, we generate random but sufficiently realistic electricity price signals so that the pre-trained DRL model is robust and adaptive to the dynamic and variable electricity prices. The validation results are encouraging and show the potential of ≈12%-15% savings in the one day cost of HVAC operation, proving the usefulness of including electricity pricing related features as state features.
A Centralised Soft Actor Critic Deep Reinforcement Learning Approach to District Demand Side Management through CityLearnA. Kathirgamanathan, K. Twardowski, E. Mangina, D. Finn (University College Dublin)Reinforcement learning is a promising model-free and adaptive controller for demand side management, as part of the future smart grid, at the district level. This paper presents the results of the algorithm that was submitted for the CityLearn Challenge, which was hosted in early 2020 with the aim of designing and tuning a reinforcement learning agent to flatten and smooth the aggregated curve of electrical demand of a district of diverse buildings. The proposed solution secured second place in the challenge using a centralised 'Soft Actor Critic' deep reinforcement learning agent that was able to handle continuous action spaces. The controller was able to achieve an averaged score of 0.967 on the challenge dataset comprising of different buildings and climates. This highlights the potential application of deep reinforcement learning as a plug-and-play style controller, that is capable of handling different climates and a heterogenous building stock, for district demand side management of buildings.
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms on a Building Energy Demand Coordination TaskG. Dhamankar, J. Vazquez-Canteli, Z. Nagy (The University of Texas at Austin)Periods of high demand for electricity can raise electricity prices for building users. Flattening the electricity demand curve reduces can reduce costs and increase resiliency. We formulate this task as a multi-agent reinforcement learning (MA-RL) problem, to be achieved through demand response and coordination of electricity consuming agents, i.e., buildings. Bechmarks for such MA-RL problems do not exist. Here, we contribute an empirical comparison of three classes of MA-RL algorithms: independent learners, centralized critics with decentralized execution, and value factorization learners. We evaluate these algorithms on an energy coordination task in CityLearn, an Open AI Gym environment. We found independent learners with shaped rewards to be competitive with more complex algorithms. Agents with centralized critics aim to learn a rich joint critic, which may complicate the training process and cause scalability issues. Our findings indicate value factorization learners possess the coordination benefits of centralized critics and match independent learners without individualized reward shaping.
Discussion
13:50-14:00Break
14:00-14:50Session 2: Clash of AlgorithmsLess is More: Simplified State-Action Space for Deep Reinforcement Learning based HVAC ControlS. Murugensan, Z. Jiang, M. Risbeck, J. Amores, C. Zhang, V. Ramamurti, K. Drees, Y. Lee (Johnson Controls Inc)How do we optimize heating, ventilation and air-conditioning (HVAC) systems for energy cost and occupant comfort? How do we accomplish this in an automated fashion that adapts with time with minimal human intervention? Answers to these questions have tremendous impact on building occupant comfort, building operating costs and, importantly, environmental footprint. Understandably, this topic has received considerable attention from experts both in the industry and the academia. Among these works, deep learning, specifically deep reinforcement learning (DRL) is emerging as a data-driven control strategy without requiring an explicit dynamic model of the system. Another advantage of DRL, when successfully developed, is that it can continue to learn and adapt as the building/HVAC characteristics change with time. DRL agents, however, are challenging to train. On one hand, they may need months or years of training data (sample inefficiency), potentially inconveniencing building occupants and incurring high energy costs for a long time. On the other hand, they may converge to local optima or simply do not converge. This paper highlights one strategy to mitigate some of these challenges. We show that the choice of state and action space is as important as the choice of DRL architectures and neural network training techniques. Specifically, we formulate the HVAC control problem as a Partially Observable Markov Decision Process (POMDP), build a DRL agent using Deep Q Networks (DQN) on a building simulator, and quantify gains over a widely adopted baseline heuristic method. Subsequently, we reformulate the original problem as a restricted POMDP by severely restricting the observation (state space) and action space, and build a DRL agent for the restricted POMDP. The performance gains from this DRL agent is double that of the original agent, implying that complex state and action spaces, while information rich, can lead to complex loss functions that could not be maneuvered well by a DRL agent. Our larger message is this: 'less' (state-action space) can be 'more', in the context of DRL training.
Continual adaptation in deep reinforcement learning-based control applied to non-stationary building environmentsA. Naug, M. Quinones, G. Biswas (Vanderbilt University)This paper develops a continual deep reinforcement relearning (RL) controller for large buildings that exhibit non-stationary behaviors. The non-stationarity in building operations caused by unexpected changes in weather patterns, occupancy, and faults, makes it imperative to develop control algorithms that adapt to the changing conditions. Given the slow time constants in building operations, we assume that the non-stationarity can be modeled as discrete transitions between stationary models of system behavior. We address the challenge of detecting transitions between stationary processes using trend analysis, and relearn control policies that accommodate the tradeoff between energy savings and comfort after such transitions occur. We demonstrate our approach on a "smart building test-bed" for developing data-driven HVAC controllers that are deployed in large buildings on our university campus.
A Comparison of Model-Free and Model Predictive Control for Price Responsive Water HeatersD. Biagioni, X. Zhang, P. Graf, D. Sigler, W. Jones (National Renewable Energy Laboratory)We present a careful comparison of two model-free control algorithms, Evolution Strategies (ES) and Proximal Policy Optimization (PPO), with receding horizon model predictive control (MPC) for operating simulated, price responsive water heaters. Four MPC variants are considered: a one-shot controller with perfect forecasting yielding optimal control; a limited-horizon controller with perfect forecasting; a mean forecasting-based controller; and a two-stage stochastic programming controller using historical scenarios. In all cases, the MPC model for water temperature and electricity price are exact; only water demand is uncertain. For comparison, both ES and PPO learn neural network-based policies by directly interacting with the simulated environment under the same scenarios used by MPC. All methods are then evaluated on a separate one-week continuation of the demand time series. We demonstrate that optimal control for this problem is challenging, requiring more than 8-hour lookahead for MPC with perfect forecasting to attain the minimum cost. Despite this challenge, both ES and PPO learn good general purpose policies that outperform mean forecast and two-stage stochastic MPC controllers in terms of average cost and are more than two orders of magnitude faster at computing actions. We show that ES in particular can leverage parallelism to learn a policy in under 90 seconds using 1150 CPU cores.
Flexible Reinforcement Learning Framework for Building Control using EnergyPlus-Modelica Energy ModelsJ. Lee, S. Huang, A. Rahman, A. Smith, S. Katipamula (Pacific Northwest National Laboratory)In recent years, reinforcement learning (RL) methods have been greatly enhanced by leveraging deep learning approaches. RL methods applied to building control have shown potential in many applications because of their ability to complement or replace conventional methods such as model-based or rule-based controls. However, RL-based building control software is likely tailored either to one target building system or to a specific RL method so that significant additional effort would be required to customize the RL-based controller for use in other building systems or with other RL approaches. Also, RL-based building controls usually depend on building energy simulations to train controllers, so emulating building dynamics (i.e., thermal dynamics and control dynamics) and capturing sub-hourly dynamic profiles are crucial to further the development of effective RL-based building control methods. To address these challenges, we present an RL-based control software employing a high-fidelity hybrid EnergyPlus-Modelica building energy model that emulates building dynamics at 1 minute resolution. This software consists of decoupled components (environment, building emulator, control agent, and RL algorithm), which allows for quick prototyping and benchmarking of standard RL algorithms in different systems; for example, a single component can be replaced without revising the software. To demonstrate this software framework, we conducted a benchmark study using an EnergyPlus-Modelica building energy model for a Chicago office building with an RL-based controller to dynamically control the chilled water temperature setpoint and the air handling unit supply air temperature setpoints.
Discussion
14:50-15:00Break
19:00-20:20Session 3: Keeping it ReaLAugmenting Reinforcement Learning with a Planning Model for Optimizing Energy Demand Response in a Prospective ExperimentL. Spangher, A. Gokul, M. Khattar, J. Palakapilly, A. Tawade, U. Agwan, C. Spanos (University of California, Berkeley)While reinforcement learning (RL) on humans has shown incredible promise, it often suffers from a scarcity of data and few steps. In instances like these, a planning model of human behavior may greatly help. We present an experimental setup for the development and testing of an Soft Actor Critic (SAC) V2 RL architecture for several different neural architectures for planning models: an autoML optimized LSTM, an OLS, and a baseline model. We present the effects of including a planning model in agent learning within a simulation of the office, currently reporting a limited success with the LSTM.
Transferable Reinforcement Learning for Smart HomesX. Zhang, X. Jin, C. Tripp, D. Biagioni, P. Graf, H. Jiang (National Renewable Energy Laboratory)To harness the great amount of untapped resources on the demand side, smart home technology plays a vital role in solving the "last mile" problem in smart grid. Reinforcement learning (RL), which has demonstrated an outstanding performance in solving many sequential decision-making problems, can be a great candidate to be used in smart home control. For instance, many studies have started investigating the appliance scheduling problem under dynamic pricing scheme. Based on those, this study aims at providing an affordable solution to encourage a higher smart home adoption rate. Specifically, we investigate combining transfer learning (TL) with RL to reduce the training cost of an optimal RL control policy. Given an optimal policy for a benchmark home, TL can jump-start the RL training of a policy for a new home, which has different appliances and user preferences. Simulation results show that by leveraging TL, RL training converges faster and requires much less computing time for new homes that are similar to the benchmark home. In all, this study proposes a cost-effective approach for training RL control policies for homes at scale, which ultimately reduces the controller's implementation costs, increases the adoption rate of RL controllers, and makes more homes grid-interactive.
Deep Reinforcement Learning in Buildings: Implicit Assumptions and their ImpactA. Prakash, S. Touzani, M. Kiran, S. Agarwal, M. Pritoni, J. Granderson (Lawrence Berkeley National Laboratory)As deep reinforcement learning (DRL) continues to gain interest in the smart building research community, there is a transition from simulation-based evaluations to deploying DRL control strategies in actual buildings. While the efficacy of a solution could depend on a particular implementation, there are common obstacles that developers have to overcome to deliver an effective controller. Additionally, a deployment in a physical building can invalidate some of the assumptions made during the controller development. Assumptions on the sensor placement or on the equipment behavior can quickly come undone. This paper presents some of the significant assumptions made during the development of DRL based controllers that could affect their operations in a physical building. Furthermore, a preliminary evaluation revealed that controllers developed with some of these assumptions can incur twice the expected costs when they are deployed in a building.
Towards Off-policy Evaluation as a Prerequisite for Real-world Reinforcement Learning in Building ControlB. Chen (Carnegie Mellon University), M. Jin (Virginia Tech), Z. Wang, T. Hong (Lawrence Berkeley National Laboratory), M. Berges (Carnegie Mellon University)We present an initial study of off-policy evaluation (OPE), a problem prerequisite to real-world reinforcement learning (RL), in the context of building control. OPE is the problem of estimating a policy's performance without running it on the actual system, using historical data from the existing controller. It enables the control engineers to ensure a new, pretrained policy satisfies the performance requirements and safety constraints of a real-world system, prior to interacting with it. While many methods have been developed for OPE, no study has evaluated which ones are suitable for building operational data, which are generated by deterministic policies and have limited coverage of the state-action space. After reviewing existing works and their assumptions, we adopted the approximate model (AM) method. Furthermore, we used bootstrapping to quantify uncertainty and correct for bias. In a simulation study, we evaluated the proposed approach on 10 policies pretrained with imitation learning. On average, the AM method estimated the energy and comfort costs with 1.84% and 14.1% error, respectively.
Discussion
15:50-16:00Closing remarksGeneral Chair and TPC Chairs-
Happy Hour
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Organization

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General Chairs

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  1. Zoltan Nagy (The University of Texas at Austin)
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Technical Program Committee Co-Chairs

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  1. Mario Berges (Carnegie Mellon University)
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  2. Bingqing Chen (Carnegie Mellon University)
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  3. June Young Park (University of Texas at Arlington)
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Technical Program Committee

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  1. Henning Lange (University of Washington)
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  2. Helia Zandi (Oak Ridge National Laboratory)
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  3. Jose Vazquez-Canteli (The University of Texas at Austin)
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  4. Zhe Wang (Lawrence Berkeley National Lab)
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  5. Duc Van Le (NTU Singapore)
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  6. Wan Du (University of California, Merced)
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  7. Xin Jin (National Renewable Energy Laboratory)
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  8. Ming Jin (University of California, Berkeley)
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  9. Alex Vlachokostas (Pacific Northwest National Laboratory)
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  10. Hari Prasanna Das (UC Berkeley)
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  11. Lucas Spangheer (University of California, Berkeley)
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  12. Kuldeep Kurte (Oak Ridge National Laboratory)
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  13. Ross May (Dalarna University)
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Location

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RLEM Workshop 2020 will be held virtually while ACM BuildSys’20 is located at Yokohama, Japan.

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+ + + + + + + + diff --git a/editions/2021/index.html b/editions/2021/index.html new file mode 100644 index 0000000..9f8b485 --- /dev/null +++ b/editions/2021/index.html @@ -0,0 +1,465 @@ + + + + + + + + RLEM Workshop 2021 - RLEM Workshop 2023 + + + + + + + + + + + + + + + + + + + + + + + +
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+ RLEM Workshop 2023 +

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+ Previous Edition: RLEM Workshop 2021 +

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Second ACM SIGEnergy Workshop on Reinforcement Learning for Energy Management in Buildings & Cities (RLEM)

+ +

About

+

RLEM brings together researchers and industry practitioners for the advancement of (deep) reinforcement learning (RL) in the built environment as it is applied for managing energy in civil infrastructure systems (energy, water, transportation).

+ +

RLEM’21 will be held in conjunction with ACM BuildSys’21. Following BuildSys’s directive, the conference will be held virtually on November 16th 2021.

+ +

:tv: Watch RLEM’21 on YouTube: Part 1 and Part 2!

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Registration

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Register now via BuildSys 2021.

+ +

Important Dates

+ + +

Call for Papers

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Buildings account for 40% of the global energy consumption and 30% of the associated greenhouse gas emissions, while also offering a 50–90% CO2 mitigation potential. The transportation sector is responsible for an additional 30%. Optimal decarbonization requires electrification of end-uses and concomitant decarbonization of electricity supply, efficient use of electricity for lighting, space heating, cooling and ventilation (HVAC), and domestic hot water generation, and upgrade of the thermal properties of buildings. A major driver for decarbonization are integration of renewable energy systems (RES) into the grid, and photovoltaics (PV) and solar-thermal collectors as well as thermal and electric storage into residential and commercial buildings. Electric vehicles (EVs), with their storage capacity and inherent connectivity, hold a great potential for integration with buildings.

+ +

The integration of these technologies must be done carefully to unlock their full potential. Artificial intelligence is regarded as a possible pathway to orchestrate these complexities of Smart Cities. In particular, (deep) reinforcement learning algorithms have seen an increased interest and have demonstrated human expert level performance in other domains, e.g., computer games. Research in the building and cities domain has been fragmented and with focus on different problems and using a variety of frameworks. The purpose of this Workshop is to build a growing community around this exciting topic, provide a platform for discussion for future research direction, and share common frameworks.

+ +

Topics of Interest

+

Topics of interest include, but are not limited to:

+ + +

Submission Instructions

+

Submitted papers must be unpublished and must not be currently under review for any other publication. Paper submissions must be at most 4 single-spaced US Letter (8.5”x11”) pages, including figures, tables, and appendices (excluding references). All submissions must use the LaTeX (preferred) or Word styles found here https://www.acm.org/publications/proceedings-template. Authors must make a good faith effort to anonymize their submissions by (1) using the “anonymous” option for the class and (2) using “anonsuppress” section where appropriate. Papers that do not meet the size, formatting, and anonymization requirements will not be reviewed. Please note that ACM uses 9-pt fonts in all conference proceedings, and the style (both LaTeX and Word) implicitly define the font size to be 9-pt.

+ + + +

Program

+

All times are in Greenwich Meridian Time (GMT).

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TimeSessionTitleSpeakerAbstract
15:00-15:20Opening remarksGeneral Chair and TPC Chairs-
15:20-16:00Keynote 1Scalable Load Management System using Reinforcement Learning +Helia Zandi (Modeling and Simulation Software Engineer at Oak Ridge National Laboratory): Helia Zandi received her M.S. in Computer Engineering from University of Florida in 2012. She is currently a Reseacrh and Development Staff in the Computational Systems Engineering Group in Computational Sciences and Engineering Division at Oak Ridge National Laboratory (ORNL). She joined ORNL as a Modeling and Simulation Software Engineer in 2016. Prior to that, she worked at ProNova Solutions as a Researcher and Software Engineer in Imaging and Positioning team. She developed sophisticated automation methodologies for accurate robot positioning and robot calibration. Her research expertise includes machine learning, robotics, advance data analytics, cyber-physical system, large-scale building-to-grid integration, and design and developing optimization and control platform with an application to buildings and energy systems.Internet of things has revolutionized the interaction between devices, actuators, sensors, and individuals in a building. Effective integration of these assets in buildings has critical role in providing load flexibility to support grid resiliency. In addition to a frequently changing load shape due to new demand patterns, the increasing penetration of renewable energy sources adds variability to power generation. The grid needs a reliable control system to coordinate the effects of these changes in real time to ensure safe and reliable operation. To address this, many optimization methods have been developed and researched. However, many of the existing approaches cannot adapt to the changes in the environment. Model-free reinforcement learning has gained a lot of attention in recent years for creating the optimal load schedule in buildings to provide benefits for building and utility stakeholders. In this talk, we will discuss a scalable software framework and various deep model-free RL algorithms that we have developed, and field tested over the past few years for optimal operation of the buildings.
16:00-16:50Session 1: Addressing challenges of applying RL in real-world buildingsSensitivity and Robustness of End-to-end Data-Driven Approach for Building Performance OptimizationA. Naug (Vanderbilt University), M. Quinones-Grueiro (Vanderbilt University), G. Biswas (Vanderbilt University)This paper discusses an approach to optimizing the performance of an end-to-end data-driven control approach for building energy management. The proposed approach, designed for systems that exhibit non-stationary behavior, involves two primary components: (1) performance degradation detection, followed by (2) relearning a set of data-driven models of the system to update the controller policy using a reinforcement learning approach. The overall control framework involves a large hyperparameter space that has to be tuned for "optimal" performance. In this paper, we analyze the sensitivity and robustness to a small set of relevant hyperparameters that have a significant impact on the overall performance. We study the performance in terms of the accuracy of the derived data-driven models that support relearning and the speed of convergence of the reinforcement learning controller.
Addressing partial observability in reinforcement learning for energy managementM. Biemann (TU Denmark), X. Liu (TU Denmark), Y. Zeng (Northumbria University), L. Huang (Norwegian University of Science and Technology)Automatic control of energy systems is affected by the uncertainties of multiple factors, including weather, prices and human activities. The literature relies on Markov-based control, taking only into account the current state. This impacts control performance, as previous states give additional context for decision making. We present two ways to learn non-Markovian policies, based on recurrent neural networks and variational inference. We evaluate the methods on a simulated data centre HVAC control task. The results show that the off-policy stochastic latent actor-critic algorithm can maintain the temperature in the predefined range within three months of training without prior knowledge while reducing energy consumption compared to Markovian policies by more than 5%.
Deep Q-Network Boosted with External Knowledge for HVAC ControlK. Jneid (Université Grenoble Alpes, LIG), S. Ploix (Grenoble INP), P. Reignier (Université Greno-ble Alpes, LIG), P. Jallon (eLichens)Heating, ventilation, and air conditioning (HVAC) systems consume nearly 40% of the total energy consumption in developed countries. Traditional techniques such as rule based control (RBC) fail to control these systems in an optimal way. Model predictive control (MPC) has been widely explored in literature as well but it doesn't represent a practical solution due to the complexity of buildings' dynamics that it relies on. Recently, deep reinforcement learning (DRL) has shown great success in the domain of optimal control such as robotics and gaming. In this paper, we develop two model-free DRL approaches to optimize the energy consumption of an office while maintaining thermal comfort and good indoor air quality through controlling the radiator and the opening/closing of a window and a door existing in the office. The two DRL approaches belong to deep-Q network (DQN): the first approach represents a DQN agent with no knowledge of the environment and the second approach represents a DQN agent with initial knowledge of the environment: A hybrid approach DQN+RBC. The goal of having external knowledge in DQN agent is to boost convergence by exploiting the RBC rules. We evaluate the performance of these two approaches against an RBC approach through simulations using a physical model of the office's dynamics. Experiments show that the two DRL approaches succeeded to maintain better thermal comfort and better indoor air quality compared with RBC approach while consuming nearly the same energy. In addition, experiments demonstrate that the DQN with knowledge outperforms the DQN with no knowledge in the beginning and converges faster to the optimal value.
16:50-17:00Break
17:00-17:40Keynote 2Data-driven Control of Complex Engineering Systems +Andrey Bernstein (Group Manager at National Renewable Energy Laboratory): Andrey Bernstein received his B.Sc., M.Sc., and PhD degrees in Electrical Engineering from the Technion - Israel Institute of Technology. Between 2010 and 2011, he was a visiting researcher at Columbia University. During 2011-2012, he was a visiting Assistant Professor at the Stony Brook University. From 2013 to 2016, he was a postdoctoral researcher at the Laboratory for Communications and Applications of Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland. Since October 2016 he has been a Senior Researcher and Group Manager at the National Renewable Energy Laboratory, Golden, CO, USA. His research interests are in the decision and control problems in complex environments and related optimization and machine learning methods, with application to power and energy systems.Optimal control of complex engineering systems is an extremely hard task. The classical approach, based on optimal control concepts (such as model-predictive control), is infeasible for large-scale systems where the accurate model is unavailable or is expensive to develop. Thus, machine learning (ML) approaches are becoming a popular alternative. In this talk, we will overview two most popular ML-based approaches, one based on reinforcement learning and another based on data-driven predictive control, their variants, and their application to optimal control of grid-interactive buildings.
17:40-18:00Community AnnouncementsCityLearn Challenge Winners, Annex#81, Climate Change AIGeneral Chair and TPC Chairs-
18:00-18:50Session 2: Benchmarking RL with other controls including other RLsComparative Analysis of Model-Free and Model-Based HVAC Control for Residential Demand ResponseK. Kurte (ORNL), K. Amasyali (ORNL), J. Munk(ORNL), H. Zandi (ORNL)In this paper, we present a comparative analysis of model-free reinforcement learning (RL) and model predictive control (MPC) approaches for intelligent control of heating, ventilation, and air-conditioning (HVAC). Deep-Q-network (DQN) is used as a candidate for model-free RL algorithm. The two control strategies were developed for residential demand-response (DR) HVAC system. We considered MPC as our golden standard to compare DQN's performance. The question we tried to answer through this work was, What % of MPC's performance can be achieved by model-free RL approach for intelligent HVAC control?. Based on our test result, RL achieved an average of ≈ 62% daily cost saving of MPC. Considering the pure optimization and model-based nature of MPC methods, the RL showed very promising performance. We believe that the interpretations derived from this comparative analysis provide useful insights to choose from various DR approaches and further enhance the performance of the RL-based methods for building energy managements.
Sinergym: A Building Simulation and Control Framework for Training Reinforcement Learning AgentsJ. Jiménez-Raboso (Universidad de Granada), A. Campoy-Nieves (Universidad de Granada), A. Manjavacas-Lucas (Universidad de Granada), J. Gómez-Romero (Universidad de Granada), M. Molina-Solana (Universidadde Granada)We introduce Sinergym, an open-source building simulation and control framework for training reinforcement learning agents. The proposed framework is compatible with EnergyPlus models and allows to implement Python-based controllers, facilitating reproducibility of experiments and generalization to multiple scenarios. A comparison between Sinergym and other existing libraries for building control is included. We describe its design and main functionalities, such as offering a diverse set of environments with different buildings, weather types and action spaces. The provided examples show the usage of the framework for benchmarking reinforcement learning methods for building control.
Collaborative energy demand response with decentralized actor and centralized criticR. Glatt (LLNL), F. Leno da Silva (LLNL), B. Soper (LLNL), W. A. Dawson (LLNL), E. Rusu (LLNL), R. A. Goldhahn (LLNL)The ongoing industrialization and rising technology adoption around the world are leading to ever higher energy consumption. The benefits of electrification are enormous, but the growing demand also comes with challenges with respect to associated greenhouse gas emissions. Although continuing progress in energy research has brought up new technologies in energy generation, storage, and distribution, most of those technologies focus on increasing efficiency of individual components. Work on integration and coordination abilities between individual components in micro-grids will lead to further improvements and gains in efficiency that are necessary to reduce carbon footprints and slow down climate change. To this end, the CityLearn environment provides a simulation framework that allows the control of energy components in buildings that are organized in districts. In this paper, we propose an energy management system based on the decentralized actor-critic reinforcement learning algorithm MARLISA but integrate a centralized critic and call it MARLISADSCC. In this way, we are training a model to autonomously control the energy storage of individual buildings in a CityLearn district to improve demand response guided by a better informed training signal. We show performance increases over baseline control techniques for a district but also discuss the resulting action selection for individual buildings.
18:50-19:00Break
19:00-20:20Session 3: Getting started with RLCOBS: COmprehensive Building SimulatorT. Zhang, O. Ardakanian-
Tools for the Design and Evaluation of Building Controls: Spawn of EnergyPlus, OpenBuildingControl, and BOPTESTD. Blum, M. Wetter-
ACTB: Advanced Controls Test BedT. Marzullo, S. Dey, N. Long, G. Henze-
CityLearn: Demand response using multi-agent reinforcement learningJ. Vazquez-Canteli-
20:20-20:30Closing remarksGeneral Chair and TPC Chairs-
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Organization

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General Chairs

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  1. +Zoltan Nagy (The University of Texas at Austin)
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Technical Program Committee Co-Chairs

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  1. +Jan Drgona (Pacific Northwest National Laboratory)
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  2. +June Young Park (University of Texas at Arlington)
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Web Chair

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  1. +Matias Quintana (National University of Singapore)
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Technical Program Committee

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  1. +Anand Krishnan Prakash (Lawrence Berkeley National Lab)
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  2. +Anjukan Kathirgamanathan (University College Dublin)
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  3. +Ankush Chakrabarty (MERL - Mitsubishi Electric Research Laboratories)
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  4. +Bharathan Balaji (Amazon)
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  5. +Bratislav Svetozarevic (Swiss Federal Laboratories for Materials Science and Technology)
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  6. +Giuseppe Pinto (Politecnico di Torino)
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  7. +Hari Prasanna Das (UC Berkeley)
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  8. +Helia Zandi (Oak Ridge National Laboratory)
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  9. +Jose Vazquez-Canteli (Mapped)
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  10. +Kuldeep Kurte (Oak Ridge National Laboratory)
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  11. +Ming Jin (Virginia Tech)
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  12. +Omid Ardakanian (University of Alberta)
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  13. +Silvio Brandi (Politecnico di Torino)
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  14. +Xin Jin (National Renewable Energy Laboratory)
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  15. +Zhe Wang (Lawrence Berkeley National Lab)
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  16. +Zhiang Zhang (University of Nottingham Ningbo China)
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Location

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RLEM Workshop 2021 will be held virtually while ACM BuildSys’21 is located at Coimbra, Portugal.

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Third ACM SIGEnergy Workshop on Reinforcement Learning for Energy Management in Buildings & Cities (RLEM)

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About

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RLEM brings together researchers and industry practitioners for the advancement of (deep) reinforcement learning (RL) in the built environment as it is applied for managing energy in civil infrastructure systems (energy, water, transportation).

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RLEM’22 will be held in conjunction with ACM BuildSys’22.

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Registration

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Register now via BuildSys 2022.

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Important Dates

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Call for Papers

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Buildings account for 40% of the global energy consumption and 30% of the associated greenhouse gas emissions, while also offering a 50–90% CO2 mitigation potential. The transportation sector is responsible for an additional 30%. Optimal decarbonization requires electrification of end-uses and concomitant decarbonization of electricity supply, efficient use of electricity for lighting, space heating, cooling and ventilation (HVAC), and domestic hot water generation, and upgrade of the thermal properties of buildings. A major driver for decarbonization are integration of renewable energy systems (RES) into the grid, and photovoltaics (PV) and solar-thermal collectors as well as thermal and electric storage into residential and commercial buildings. Electric vehicles (EVs), with their storage capacity and inherent connectivity, hold a great potential for integration with buildings.

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The integration of these technologies must be done carefully to unlock their full potential. Artificial intelligence is regarded as a possible pathway to orchestrate these complexities of Smart Cities. In particular, (deep) reinforcement learning algorithms have seen an increased interest and have demonstrated human expert level performance in other domains, e.g., computer games. Research in the building and cities domain has been fragmented and with focus on different problems and using a variety of frameworks. The purpose of this Workshop is to build a growing community around this exciting topic, provide a platform for discussion for future research direction, and share common frameworks.

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Topics of Interest

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Topics of interest include, but are not limited to:

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Submission Instructions

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Submitted papers must be unpublished and must not be currently under review for any other publication. Paper submissions must be at most 4 single-spaced US Letter (8.5”x11”) pages, including figures, tables, and appendices (excluding references). All submissions must use the LaTeX (preferred) or Word styles found here https://www.acm.org/publications/proceedings-template. Authors must make a good faith effort to anonymize their submissions by (1) using the “anonymous” option for the class and (2) using “anonsuppress” section where appropriate. Papers that do not meet the size, formatting, and anonymization requirements will not be reviewed. Please note that ACM uses 9-pt fonts in all conference proceedings, and the style (both LaTeX and Word) implicitly define the font size to be 9-pt.

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Program

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All times are in Eastern Standard Time (EST).

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TimeSessionTitleSpeakerAbstract
08:00-08:10IntroductionZoltan Nagy-
08:10-08:55Keynote 1Architecting a Path Toward Generalized Autonomy: Addressing the Biggest Opportunity in DecarbonizationTroy Harvey (CEO and co-founder of PassiveLogic): is the creator of the first platform for generalized autonomy. As architect of the Quantum Digital Twin standard and Deep Physics AI engine, his empathic, systems-oriented approach to technology development is transforming the way we control systems and equipment. Optimizing buildings, cities, and other controlled systems is the clearest opportunity Troy sees to contribute to the world's most pressing climate challenges.-.
08:55-09:05Break
09:05-09:50Session 1Behavioural cloning based RL agents for district energy managementSharath Ram Kumar, Arvind Easwaran, Benoit Delinchant, Rémy Rigo-MarianiIn this work, we discuss a method to incorporate domain knowledge into a Reinforcement Learning (RL) environment through the process of behavioral cloning, in the context of a district energy management system. Prior knowledge, encoded into heuristic rule-based programs, is used to initialize a policy network for an RL agent, after which an RL algorithm is used to improve on this by optimizing against a reward function. The key ideas are implemented in the CityLearn framework, where the resulting controller is used to manage the electrical energy storage for 5 buildings in a district. We demonstrate that the resulting agents offer measurable performance gains compared to existing baselines, offering a 3.8% improvement over the underlying rule-based controller, and a 20% improvement over a pure RL based controller. We also illustrate the possibility of using imitation learning to develop agents with desirable characteristics without explicit reward shaping.
Deep reinforcement learning-based SOH-aware battery management for DER aggregationShotaro Nonaka, Daichi Watari, Ittetsu Taniguchi, Takao OnoyeIn smart energy systems, batteries, which assume an important role in filling the temporal gap between generation and consumption, are expected to be a potential distributed energy resource (DER). A resource aggregator (RA) has emerged to collect various DERs to extract demand-side flexibility, and various methods have been proposed based on reinforcement learning. Since battery degradation is unavoidable during utilization, battery management is required to minimize it. This paper proposes state-of-health (SOH)-aware battery management based on deep reinforcement learning. Our experimental results demonstrate an average battery lifetime improvement of 11.2%.
Deep reinforcement learning with online data augmentation to improve sample efficiency for intelligent HVAC controlKuldeep R Kurte, Kadir Amasyali, Jeffrey Munk, Helia ZandiDeep Reinforcement Learning (DRL) has started showing success in real-world applications such as building energy optimization. Much of the research in this space utilized simulated environments to train RL-agent in an offline mode. Very few research have used DRL-based control in real-world systems due to two main reasons: 1) sample efficiency challenge---DRL approaches need to perform a lot of interactions with the environment to collect sufficient experiences to learn from, which is difficult in real systems, and 2) comfort or safety related constraints---user's comfort must never or at least rarely be violated. In this work, we propose a novel deep Reinforcement Learning framework with online Data Augmentation (RLDA) to address the sample efficiency challenge of real-world RL. We used a time series Generative Adversarial Network (TimeGAN) architecture as a data generator. We further evaluated the proposed RLDA framework using a case study of an intelligent HVAC control. With a ≈28% improvement in the sample efficiency, RLDA framework lays the way towards increased adoption of DRL-based intelligent control in real-world building energy management systems.
09:50-10:00Break
10:00-10:45Keynote 2Keynote: Opportunities and Challenges of Building Energy Management using Advanced Building ControlsGregor P. Henze, Ph.D., P.E. (Professor and C.V. Schelke Chair in the Department of Civil, Environmental and Architectural Engineering at the University of Colorado): His teaching focuses on the building energy systems side of architectural engineering, i.e., thermal environmental engineering, HVAC and refrigeration systems, design of energy efficient buildings, building control and automation systems, data science for building engineering applications, and sustainable building design. His research emphasizes model predictive control and reinforcement learning control of building energy systems, building thermal mass, refrigeration systems, model-based benchmarking of building operational performance, fault detection and diagnosis, control strategies for mixed-mode buildings, uncertainty quantification of occupant behavior and its impact, human presence detection, sensor fusion algorithms, energy analytics and decision analysis as well as the integration of building energy system operations with the electric grid system. He is the primary author of more than 150 research articles, four of which have received best paper awards, and received three patents. He received the 2011 Colorado Cleantech Industry Association's Research and Commercialization Award. Prof. Henze is a professional mechanical engineer, certified high-performance building design professional (HBDP), editorial board member for Journal Building Performance Simulation, Fellow of the Renewable and Sustainable Energy Institute, joint professor at the National Renewable Energy Laboratory, collaborating with the power systems engineering and building research groups, as well as co-founder and chief scientist of QCoefficient, Inc., a startup developing real-time optimal control solutions for grid-interactive efficient buildings. Finally, he is the Fulbright Distinguished Chair in Science, Technology, and Innovation at CSIRO in Newcastle, Australia for 2022.-
10:45-11:15Session 2B2RL: an open-source dataset for building batch reinforcement learningHsinyu Liu, Xiaohan Fu, Bharathan Balaji, Rajesh K. Gupta, Dezhi HongBatch reinforcement learning (BRL) is an emerging research area in the RL community. It learns exclusively from static datasets (i.e. replay buffers) without interaction with the environment. In the offline settings, existing replay experiences are used as prior knowledge for BRL models to find the optimal policy. Thus, generating replay buffers is crucial for BRL model benchmark. In our B2RL (Building Batch RL) dataset, we collected real-world data from our building management systems, as well as buffers generated by several behavioral policies in simulation environments. We believe it could help building experts on BRL research. To the best of our knowledge, we are the first to open-source building datasets for the purpose of BRL learning.
ComfortLearn: enabling agent-based occupant-centric building controlsMatias Quintana, Zoltan Nagy, Federico Tartarini, Stefano Schiavon, Clayton MillerThe intersection of buildings control and thermal comfort modeling may seem obvious, but there are still prevalent challenges in combining them. "Occupant centric" control strategies are mainly trained using building data but rarely leverage occupants' feedback. While thermal comfort models are developed using occupants' data but are seldom integrated into building controls. To bridge this gap, we developed an open-source simulation tool named ComfortLearn. ComfortLearn is an OpenAI Gym-based environment that leverages historical building management system data from real buildings and existing longitudinal thermal comfort datasets for occupant-centric control strategies and benchmarking. We used an evaluation metric named 'exceedance' to evaluate occupants' thermal comfort and provide a more realistic picture than traditional evaluations like comfort bands. This setup allows the analysis of different building control strategies and their effect on real occupants, based on empirical data, without the need for computationally expensive co-simulations. A theoretical case study implementation shows that an as-is schedule-based controller complies with its comfort band more than 93% of the time, but the simulated occupants are comfortable for only 25% of the occupied time.
11:15-11:25Break
11:25-12:10Panel Discussion
12:10-12:15ClosingZoltan Nagy-
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Organization

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General Chairs

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  1. Zoltan Nagy (The University of Texas at Austin)
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  2. Jan Drgona (Pacific Northwest National Laboratory)
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  1. Kuldeep Kurte (Oak Ridge National Laboratory)
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  2. Matias Quintana (National University of Singapore)
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  3. Yaxin Bi (Ulster University at Jordanstown)
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Web Chair

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  1. Kingsley Nweye (The University of Texas at Austin)
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Technical Program Committee

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  1. Helia Zandi (Oak Ridge National Laboratory)
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  2. Himanshu Sharma (Pacific Northwest National Laboratory)
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  3. June Young Park (The University of Texas at Arlington)
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  4. Kadir Amasyali (Oak Ridge National Laboratory)
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Location

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RLEM Workshop 2022 will be held in hybrid (virtual and in-person). The Zoom link for virtual workshop participants will be communicated. Location for in-person workshop participants is:

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Hilton Boston Back Bay
+40 Dalton St, Boston, MA 02115
+Room: Mariner

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See here for more ACM BuildSys venue, travel and visa information.

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Fourth ACM SIGEnergy Workshop on Reinforcement Learning for Energy Management in Buildings & Cities (RLEM)

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RLEM brings together researchers and industry practitioners for the advancement of (deep) reinforcement learning (RL) in the built environment as it is applied for managing energy in civil infrastructure systems (energy, water, transportation).

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RLEM’23 will be held in conjunction with ACM BuildSys’23.

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Registration

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Register now via BuildSys 2023.

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Keynote

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:mega: Pleased to announce Bharathan Balaji (Amazon) and John Sipple (Google) as keynote speakers at RLEM’23!

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Venue

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The RLEM Workshop will be held virtually. The meeting link will be communicated soon.

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See here for other ACM BuildSys’23 venue, travel and visa information.

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+ Technical Program Committee + +

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+ + + + + + + + diff --git a/preprocessing/preprocess.ipynb b/preprocessing/preprocess.ipynb new file mode 100644 index 0000000..a05b3a6 --- /dev/null +++ b/preprocessing/preprocess.ipynb @@ -0,0 +1,92 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "import os\n", + "import pandas as pd\n", + "import yaml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Speaker and Talk Markdown Files\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "author_data = pd.read_csv('rlem23-authors.csv')\n", + "author_data['name'] = author_data['first'] + ' ' + author_data['last']\n", + "\n", + "\n", + "for i, ((p, t), pdata) in enumerate(author_data.groupby(['paper', 'title'])):\n", + " front_matter = {\n", + " 'name': t,\n", + " 'speakers': pdata['name'].tolist(),\n", + " 'categories': ['presentation'],\n", + " 'permalink': f'/:collection/:categories/Presentation {i + 1}',\n", + " }\n", + " front_matter = yaml.dump(front_matter, sort_keys=False)\n", + " talk_text = f'---\\n{front_matter}---\\n\\n# Abstract\\nTBD'\n", + " filepath = os.path.join('..', '_talks', f'presentation-{i + 1}.md')\n", + "\n", + " with open(filepath, 'w') as f:\n", + " f.write(talk_text)\n", + "\n", + " for j, (f, l, e) in enumerate(pdata[['first', 'last', 'email']].to_records(index=False)):\n", + " front_matter = {\n", + " 'name': f'{f} {l}',\n", + " 'first_name': f,\n", + " 'last_name': l,\n", + " 'email': None if not isinstance(e, str) and math.isnan(e) else e,\n", + " 'permalink': f'/:collection/:categories/Presentation {i + 1} Speaker {j + 1}'\n", + " }\n", + " front_matter = yaml.dump(front_matter, sort_keys=False)\n", + " speaker_text = f'---\\n{front_matter}---'\n", + " filepath = os.path.join('..', '_speakers', f'presentation-{i + 1}_speaker-{j + 1}.md')\n", + "\n", + " with open(filepath, 'w') as f:\n", + " f.write(speaker_text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/preprocessing/rlem23-authors.csv b/preprocessing/rlem23-authors.csv new file mode 100644 index 0000000..6d6e3bb --- /dev/null +++ b/preprocessing/rlem23-authors.csv @@ -0,0 +1,21 @@ +paper,title,first,last,email,affiliation,country,iscontact +22,Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households,Gargya,Gokhale,gargya.gokhale@ugent.be,"IDLab, Ghent University -imec",Belgium,yes +22,Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households,Niels,Tiben,,"IDLab, Ghent University -imec" +22,Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households,Marie-Sophie,Verwee,mariesophie.verwee@ugent.be,"IDLab, Ghent University -imec",,yes +22,Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households,Manu,Lahariya,,"IDLab, Ghent University -imec" +22,Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households,Bert,Claessens,,"IDLab, Ghent University -imec" +22,Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households,Chris,Develder,chris.develder@ugent.be,"IDLab, Ghent University -imec",,yes +54,A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real Buildings,Judah,Goldfeder,judahg@google.com,Google,United States of America,yes +54,A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real Buildings,John,Sipple,sipple@google.com,Google,,yes +71,TBMEnv - An Environment to Assess the Accuracy of Thermal Building Models under Realistic Conditions,David,"Wölfle",woelfle@fzi.de,FZI Research Center for Information Technology,Germany,yes +71,TBMEnv - An Environment to Assess the Accuracy of Thermal Building Models under Realistic Conditions,Samed,"Voßberg",vossberg@fzi.de,"FZI Research Center for \\Information Technology",,yes +71,TBMEnv - An Environment to Assess the Accuracy of Thermal Building Models under Realistic Conditions,Hartmut,Schmeck,hartmut.schmeck@kit.edu,Karlsruhe Institute of Technology,Germany,yes +75,Smart Home Energy Cost Minimisation Using Energy Trading with Deep Reinforcement Learning,Matic,Pokorn,matic.pokorn@gmail.com,Jozef Stefan Institute,Slovenia,yes +75,Smart Home Energy Cost Minimisation Using Energy Trading with Deep Reinforcement Learning,Mihael,"Mohorčič",miha.mohorcic@ijs.si,Jozef Stefan Institute,,yes +75,Smart Home Energy Cost Minimisation Using Energy Trading with Deep Reinforcement Learning,Andrej,"Čampa",andrej.campa@ijs.si,ComSensus d.o.o.,,yes +75,Smart Home Energy Cost Minimisation Using Energy Trading with Deep Reinforcement Learning,Jernej,Hribar,jernej.hribar@ijs.si,Jozef Stefan Institute,,yes +94,"Economizer Optimization with Reinforcement Learning: An Industry Perspective",Jiaron,Cui,ivancui@amazon.com,Amazon,United States of America,yes +94,"Economizer Optimization with Reinforcement Learning: An Industry Perspective",Bharathan,Balaji,bhabalaj@amazon.com,Amazon,United States of America,yes +94,"Economizer Optimization with Reinforcement Learning: An Industry Perspective",Jake,Chen,jakechen@amazon.com,Amazon,,yes +94,"Economizer Optimization with Reinforcement Learning: An Industry Perspective",Charles,Prosper,cpro@amazon.com,Amazon,,yes +94,"Economizer Optimization with Reinforcement Learning: An Industry Perspective",Wei Yih,Yap,yihyap@amazon.com,Amazon,,yes diff --git a/program/index.html b/program/index.html new file mode 100644 index 0000000..3d536ff --- /dev/null +++ b/program/index.html @@ -0,0 +1,706 @@ + + + + + + + + Program - RLEM Workshop 2023 + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+

+ RLEM Workshop 2023 +

+
+
+ + + + + Document + + + +
+

Program

+ +

The workshop will be held on November 12, 2023 and times are with respect to Pacific Time. See here for start time for other time zones.

+ + +
+ + + + + + + + +
#Room
+
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
8:00 + ! + Live + +

Introduction +

+

Zoltan Nagy +

+

+

+
+ :5 +
+ :10 + + ! + Live + +

Deploying Reinforcement Learning for Energy Management at Scale +

+

Bharathan Balaji +

+

+

+ 		+ ! + Live + +

Economizer Optimization with Reinforcement Learning: An Industry Perspective +

+

Jiaron Cui, Bharathan Balaji, Jake Chen, Charles Prosper, Wei Yih Yap +

+

+

+
+ :15 +
+ :20 +
+ :25 +
+ :30 +
+ :35 +
+ :40 + + ! + Live + +

Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households +

+

Gargya Gokhale, Niels Tiben, Marie-Sophie Verwee, Manu Lahariya, Bert Claessens, Chris Develder +

+

+

+
+ :45 +
+ :50 +
+ :55 + + ! + Live + +

Smart Home Energy Cost Minimisation Using Energy Trading with Deep Reinforcement Learning +

+

Matic Pokorn, Mihael Mohorčič, Andrej Čampa, Jernej Hribar +

+

+

+
9:00
+ :5 +
+ :10 + + ! + Live + +

Break +

+

+

+

+

+
+ :15 +
+ :20 + + ! + Live + +

Is the Agent better than the Baseline? Google's Experiment Design for deploying Optimal Control on Smart Buildings +

+

John Sipple +

+

+

+
+ :25 +
+ :30 +
+ :35 +
+ :40 +
+ :45 +
+ :50 + + ! + Live + +

A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real Buildings +

+

Judah Goldfeder, John Sipple +

+

+

+
+ :55 +
10:00
+ :5 + + ! + Live + +

TBMEnv - An Environment to Assess the Accuracy of Thermal Building Models under Realistic Conditions +

+

David Wölfle, Samed Voßberg, Hartmut Schmeck +

+

+

+
+ :10 +
+ :15 +
+ :20 + + ! + Live + +

Break +

+

+

+

+

+
+ :25 +
+ :30 + + ! + Live + +

CityLearn: A Tutorial on Reinforcement Learning Control for Grid-Interactive Efficient Buildings and Communities +

+

Kingsley Nweye +

+

+

+ 		+ ! + Live + +

Introduction to the BOPTEST Framework for Testing and Benchmarking Advanced Controllers +

+

Javier Arroyo, David Blum, Kyle Benne +

+

+

+
+ :35 +
+ :40 +
+ :45 +
+ :50 +
+ :55 +
11:00
+ :5 +
+ :10 +
+ :15 +
+ :20 +
+ :25 +
+ :30 +
+ :35 +
+ :40 +
+ :45 +
+ :50 +
+ :55 +
12:00
+ :5 +
+ :10 +
+ :15 +
+ :20 +
+ :25 +
+ :30 +
+ :35 +
+ :40 +
+ :45 +
+ :50 +
+ :55 +
13:00
+ :5 +
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+ :20 +
+ :25 +
+ :30 +
+ :35 +
+ :40 +
+ :45 +
+ :50 +
+ :55 +
+
+
Caption
+
presentation
+
keynote
+
tutorial
+
other
+
+ + + + + + + + diff --git a/rooms/room/index.html b/rooms/room/index.html new file mode 100644 index 0000000..7bab961 --- /dev/null +++ b/rooms/room/index.html @@ -0,0 +1,393 @@ + + + + + + + + Room - RLEM Workshop 2023 + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+

+ RLEM Workshop 2023 +

+
+
+ + + + + Document + + + +
+

+ Room +

+ + +

+ Rooms +

+ + + + + + + + + + + + + + + + + + +
+ Talks in this room: +
+ + + +
Sunday
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+  8:00 + ! + Live + +

Introduction

+

Zoltan Nagy

+
+  8:10 + ! + Live + +

Deploying Reinforcement Learning for Energy Management at Scale

+

Bharathan Balaji

+
+  8:10 + ! + Live + +

Economizer Optimization with Reinforcement Learning: An Industry Perspective

+

Jiaron Cui, Bharathan Balaji, Jake Chen, Charles Prosper, Wei Yih Yap

+
+  8:40 + ! + Live + +

Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households

+

Gargya Gokhale, Niels Tiben, Marie-Sophie Verwee, Manu Lahariya, Bert Claessens, Chris Develder

+
+  8:55 + ! + Live + +

Smart Home Energy Cost Minimisation Using Energy Trading with Deep Reinforcement Learning

+

Matic Pokorn, Mihael Mohorčič, Andrej Čampa, Jernej Hribar

+
+  9:10 + ! + Live + +

Break

+

+
+  9:20 + ! + Live + +

Is the Agent better than the Baseline? Google's Experiment Design for deploying Optimal Control on Smart Buildings

+

John Sipple

+
+  9:50 + ! + Live + +

A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real Buildings

+

Judah Goldfeder, John Sipple

+
+  10:5 + ! + Live + +

TBMEnv - An Environment to Assess the Accuracy of Thermal Building Models under Realistic Conditions

+

David Wölfle, Samed Voßberg, Hartmut Schmeck

+
+  10:20 + ! + Live + +

Break

+

+
+  10:30 + ! + Live + +

CityLearn: A Tutorial on Reinforcement Learning Control for Grid-Interactive Efficient Buildings and Communities

+

Kingsley Nweye

+
+  10:30 + ! + Live + +

Introduction to the BOPTEST Framework for Testing and Benchmarking Advanced Controllers

+

Javier Arroyo, David Blum, Kyle Benne

+
+ +
+ + + + + + + diff --git a/speakers/Introduction Speaker.html b/speakers/Introduction Speaker.html new file mode 100644 index 0000000..2e57952 --- /dev/null +++ b/speakers/Introduction Speaker.html @@ -0,0 +1,322 @@ + + + + + + + + Zoltan Nagy - RLEM Workshop 2023 + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+

+ RLEM Workshop 2023 +

+
+
+ + + + + Document + + + +

+ Speaker +

+ +

+ Zoltan Nagy +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Talks at this conference:
+ + + + +
+  8:00 + Introduction + ! + Live
+ + +

+ +  Overview + +

+
+ + + + + + + diff --git a/speakers/Presentation 1 Speaker 1.html b/speakers/Presentation 1 Speaker 1.html new file mode 100644 index 0000000..a3f1e28 --- /dev/null +++ b/speakers/Presentation 1 Speaker 1.html @@ -0,0 +1,311 @@ + + + + + + + + Gargya Gokhale - RLEM Workshop 2023 + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+

+ RLEM Workshop 2023 +

+
+
+ + + + + Document + + + +

+ Speaker +

+ +

+ Gargya Gokhale +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Talks at this conference:
+ + + + +
+  8:40 + Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households + ! + Live
+ + +

+ +  Overview + +

+
+ + + + + + + diff --git a/speakers/Presentation 1 Speaker 2.html b/speakers/Presentation 1 Speaker 2.html new file mode 100644 index 0000000..2ca37b9 --- /dev/null +++ b/speakers/Presentation 1 Speaker 2.html @@ -0,0 +1,313 @@ + + + + + + + + Niels Tiben - RLEM Workshop 2023 + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+

+ RLEM Workshop 2023 +

+
+
+ + + + + Document + + + +

+ Speaker +

+ +

+ Niels Tiben +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Talks at this conference:
+ + + + +
+  8:40 + Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households + ! + Live
+ + +

+ +  Overview + +

+
+ + + + + + + diff --git a/speakers/Presentation 1 Speaker 3.html b/speakers/Presentation 1 Speaker 3.html new file mode 100644 index 0000000..bdefe2b --- /dev/null +++ b/speakers/Presentation 1 Speaker 3.html @@ -0,0 +1,315 @@ + + + + + + + + Marie-Sophie Verwee - RLEM Workshop 2023 + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+

+ RLEM Workshop 2023 +

+
+
+ + + + + Document + + + +

+ Speaker +

+ +

+ Marie-Sophie Verwee +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Talks at this conference:
+ + + + +
+  8:40 + Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households + ! + Live
+ + +

+ +  Overview + +

+
+ + + + + + + diff --git a/speakers/Presentation 1 Speaker 4.html b/speakers/Presentation 1 Speaker 4.html new file mode 100644 index 0000000..7262b31 --- /dev/null +++ b/speakers/Presentation 1 Speaker 4.html @@ -0,0 +1,317 @@ + + + + + + + + Manu Lahariya - RLEM Workshop 2023 + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+

+ RLEM Workshop 2023 +

+
+
+ + + + + Document + + + +

+ Speaker +

+ +

+ Manu Lahariya +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Talks at this conference:
+ + + + +
+  8:40 + Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households + ! + Live
+ + +

+ +  Overview + +

+
+ + + + + + + diff --git a/speakers/Presentation 1 Speaker 5.html b/speakers/Presentation 1 Speaker 5.html new file mode 100644 index 0000000..cb1134c --- /dev/null +++ b/speakers/Presentation 1 Speaker 5.html @@ -0,0 +1,319 @@ + + + + + + + + Bert Claessens - RLEM Workshop 2023 + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+

+ RLEM Workshop 2023 +

+
+
+ + + + + Document + + + +

+ Speaker +

+ +

+ Bert Claessens +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Talks at this conference:
+ + + + +
+  8:40 + Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households + ! + Live
+ + +

+ +  Overview + +

+
+ + + + + + + diff --git a/speakers/Presentation 1 Speaker 6.html b/speakers/Presentation 1 Speaker 6.html new file mode 100644 index 0000000..108de22 --- /dev/null +++ b/speakers/Presentation 1 Speaker 6.html @@ -0,0 +1,321 @@ + + + + + + + + Chris Develder - RLEM Workshop 2023 + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+

+ RLEM Workshop 2023 +

+
+
+ + + + + Document + + + +

+ Speaker +

+ +

+ Chris Develder +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Talks at this conference:
+ + + + +
+  8:40 + Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households + ! + Live
+ + +

+ +  Overview + +

+
+ + + + + + + diff --git a/speakers/Presentation 2 Speaker 1.html b/speakers/Presentation 2 Speaker 1.html new file mode 100644 index 0000000..972cbae --- /dev/null +++ b/speakers/Presentation 2 Speaker 1.html @@ -0,0 +1,319 @@ + + + + + + + + Judah Goldfeder - RLEM Workshop 2023 + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+

+ RLEM Workshop 2023 +

+
+
+ + + + + Document + + + +

+ Speaker +

+ +

+ Judah Goldfeder +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Talks at this conference:
+ + + + +
+  9:50 + A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real Buildings + ! + Live
+ + +

+ +  Overview + +

+
+ + + + + + + diff --git a/speakers/Presentation 2 Speaker 2.html b/speakers/Presentation 2 Speaker 2.html new file mode 100644 index 0000000..d4c8192 --- /dev/null +++ b/speakers/Presentation 2 Speaker 2.html @@ -0,0 +1,332 @@ + + + + + + + + John Sipple - RLEM Workshop 2023 + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+

+ RLEM Workshop 2023 +

+
+
+ + + + + Document + + + +

+ Speaker +

+ +

+ John Sipple +

+ +
+ +

As a Tech Lead and Staff Software Engineer in Google Core Enterprise Machine Learning, John Sipple is on a mission to deploy novel fault detection and diagnostics and practical smart control to large-scale industrial problems. John leads multiple development efforts that combine multidimensional anomaly detection with model explainability. He also leads a research effort to deploy reinforcement learning to make commercial office buildings more efficient and environmentally sustainable. John has also worked on dialog summarization models for Google chat. As an adjunct faculty member, John also teaches Machine Learning at The George Washington University. Before joining Google, John developed and applied algorithms, statistical analysis, and machine learning solutions to cybersecurity, agriculture, counterfeit detection, and missile defense.

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Talks at this conference:
+ + + + + + + +
+  9:20 + Is the Agent better than the Baseline? Google's Experiment Design for deploying Optimal Control on Smart Buildings + ! + Live
+  9:50 + A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real Buildings + ! + Live
+ + +

+ +  Overview + +

+
+ + + + + + + diff --git a/speakers/Presentation 3 Speaker 1.html b/speakers/Presentation 3 Speaker 1.html new file mode 100644 index 0000000..8fccb72 --- /dev/null +++ b/speakers/Presentation 3 Speaker 1.html @@ -0,0 +1,317 @@ + + + + + + + + David Wölfle - RLEM Workshop 2023 + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+

+ RLEM Workshop 2023 +

+
+
+ + + + + Document + + + +

+ Speaker +

+ +

+ David Wölfle +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Talks at this conference:
+ + + + +
+  10:5 + TBMEnv - An Environment to Assess the Accuracy of Thermal Building Models under Realistic Conditions + ! + Live
+ + +

+ +  Overview + +

+
+ + + + + + + diff --git a/speakers/Presentation 3 Speaker 2.html b/speakers/Presentation 3 Speaker 2.html new file mode 100644 index 0000000..b9a66c0 --- /dev/null +++ b/speakers/Presentation 3 Speaker 2.html @@ -0,0 +1,319 @@ + + + + + + + + Samed Voßberg - RLEM Workshop 2023 + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+

+ RLEM Workshop 2023 +

+
+
+ + + + + Document + + + +

+ Speaker +

+ +

+ Samed Voßberg +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Talks at this conference:
+ + + + +
+  10:5 + TBMEnv - An Environment to Assess the Accuracy of Thermal Building Models under Realistic Conditions + ! + Live
+ + +

+ +  Overview + +

+
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+  10:5 + TBMEnv - An Environment to Assess the Accuracy of Thermal Building Models under Realistic Conditions + ! + Live
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+ Bharathan Balaji +

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Bharathan Balaji is a Senior Applied Scientist at Amazon that works on developing methods and systems that leverage machine learning (ML) to address global challenges such as waste reduction, education, and automation. He has over 5 years of experience in applied research and innovation, with a PhD in Computer Science and Engineering from UC San Diego. His core interests include machine learning (ML), smart buildings, internet of things (IoT), and sustainability. He has contributed to the launch and growth of AWS products such as SageMaker RL and DeepRacer, enabling customers across various domains to solve complex problems with RL (reinforcement learning). He has also collaborated with leading academic institutions and published 62 papers in top-tier venues, with more than 3000 citations. He is passionate about making a positive impact on the world through ML and technology.

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Talks at this conference:
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+  8:10 + Deploying Reinforcement Learning for Energy Management at Scale + ! + Live
+  8:10 + Economizer Optimization with Reinforcement Learning: An Industry Perspective + ! + Live
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+  8:10 + Economizer Optimization with Reinforcement Learning: An Industry Perspective + ! + Live
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Talks at this conference:
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+  10:30 + Introduction to the BOPTEST Framework for Testing and Benchmarking Advanced Controllers + ! + Live
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+  10:30 + Introduction to the BOPTEST Framework for Testing and Benchmarking Advanced Controllers + ! + Live
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A

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B

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C

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Deploying Reinforcement Learning for Energy Management at Scale

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Is the Agent better than the Baseline? Google's Experiment Design for deploying Optimal Control on Smart Buildings

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+ John Sipple +

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Closing

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+ Zoltan Nagy +

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+ +
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Introduction

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+ + + presentation + +
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Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households

+ +

+ Gargya Gokhale, Niels Tiben, Marie-Sophie Verwee, Manu Lahariya, Bert Claessens, Chris Develder +

+ +
+ + + + at +  8:40 + ! + Livein  Roomfor +  15min + + + + + + + + + + + + + +
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Abstract

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TBD

+ + + + + + + + +

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A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real Buildings

+ +

+ Judah Goldfeder, John Sipple +

+ +
+ + + + + + + + at +  9:50 + ! + Livein  Roomfor +  15min + + + + + + + + + +
+ +

Abstract

+

TBD

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+
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+
+ + + presentation + +
+ + +
+ +

TBMEnv - An Environment to Assess the Accuracy of Thermal Building Models under Realistic Conditions

+ +

+ David Wölfle, Samed Voßberg, Hartmut Schmeck +

+ +
+ + + + + + + + + at +  10:5 + ! + Livein  Roomfor +  15min + + + + + + + + +
+ +

Abstract

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TBD

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+ + + + + Document + + + +
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+ + + presentation + +
+ + +
+ +

Smart Home Energy Cost Minimisation Using Energy Trading with Deep Reinforcement Learning

+ +

+ Matic Pokorn, Mihael Mohorčič, Andrej Čampa, Jernej Hribar +

+ +
+ + + + + at +  8:55 + ! + Livein  Roomfor +  15min + + + + + + + + + + + + +
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Abstract

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TBD

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+ RLEM Workshop 2023 +

+
+
+ + + + + Document + + + +
+
+ + + presentation + +
+ + +
+ +

Economizer Optimization with Reinforcement Learning: An Industry Perspective

+ +

+ Jiaron Cui, Bharathan Balaji, Jake Chen, Charles Prosper, Wei Yih Yap +

+ +
+ + + at +  8:10 + ! + Livein  Roomfor +  30min + + + + + + + + + + + + + + +
+ +

Abstract

+

TBD

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+ +  Overview + + + +  Program + +

+
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+ +
+

+ RLEM Workshop 2023 +

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+
+ + + + + Document + + + +
+
+ + + tutorial + +
+ + +
+ +

CityLearn: A Tutorial on Reinforcement Learning Control for Grid-Interactive Efficient Buildings and Communities

+ +

+ Kingsley Nweye +

+ +
+ + + + + + + + + + + at +  10:30 + ! + Livein  Roomfor +  120min + + + + + + +
+ +

Description

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The CityLearn tutorial at RLEM'23 will help participants get acquainted with the CityLearn OpenAI Gym environment, developed for easy implementation and benchmarking of control algorithms, e.g., rule-based control, model predictive control or deep reinforcement learning control in the demand response, building energy and grid-interactive community domain. By the end of the tutorial, participants will learn how to design their own simple or advanced control algorithms to provide energy flexibility, and acquire familiarity with the CityLearn environment for extended use in personal projects.

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Learning Outcomes

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The primary learning outcome for participants is to gain familiarity with CityLearn environment, its application programming interface (API) and dataset offerings for extended use in academic research or personal projects. Other secondary outcomes are to:

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  1. Understand how electrification, distributed energy resources e.g. batteries, photovoltaic (PV) systems and smart controls provide a promising pathway to decarbonization and energy flexibility.
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  2. Learn how to design and optimize their own rule-based control (RBC) agent for battery management using readily available knowledge of a building's energy use.
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  3. Identify the challenges surrounding the generalizability of an RBC agent and how reinforcement learning (RL) can mitigate these challenges.
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  4. Train their own RL Tabular Q-Learning algorithm.
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  5. Evaluate the performance of a standard model-free deep RL algorithm in optimizing key performance indicators (KPIs) that are targeted at quantifying energy flexibility, environmental and economic costs.
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  6. Learn the effect of different control algorithms and their parameters in improving these KPIs.
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  7. Make submission to The CityLearn Challenge 2023.
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Target Audience

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The target audience for this tutorial includes the following:

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Prerequisites

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The CityLearn tutorial has a fairly low entry level and participants do not need to have prior experience in reinforcement learning (RL) nor use of a Gym environment. However, participants need to have at least, beginner knowledge in Python or other similar high-level scripting language. Also, participants should have a computer that is able to launch a Google Colab notebook in the browser or a Jupyter notebook locally.

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Program

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The workshop is divided into the following parts (note that durations are flexible):

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DurationDescription
5mOverview, Learning Outcomes, Climate Impact, Target Audience and Prerequisites
20mBackground on Grid-Interactive Efficient Buildings, Energy Flexibility and CityLearn
15mOverview of Hands-On Experiments, Setting up Development Environment, Dataset Description and Key Performance Indicators for Evaluation
5mCoffee Break
10mExperiment 1: Build your Custom Rule-Based Controller
10mExperiment 2: An Introduction to Tabular Q-Learning Algorithm as an Adaptive Controller
10mExperiment 3.1: Optimize a Soft-Actor Critic Reinforcement Learning Controller
15mExperiment 3.2: Tune your Soft-Actor Critic Agent
5mCoffee Break
20mMake Submission to The CityLearn Challenge 2023 Control and Forecast Tracks
5mNext Steps and Concluding Remarks
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References

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+ RLEM Workshop 2023 +

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Introduction to the BOPTEST Framework for Testing and Benchmarking Advanced Controllers

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+ Javier Arroyo, David Blum, Kyle Benne +

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Description

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The Building Optimization Testing (BOPTEST) framework is used to evaluate the performance of building Heating Ventilation and Air Conditioning (HVAC) control algorithms by enabling co-simulation between detailed building models and external controllers written by users in separate software. The framework provides a standard simulation environment and is organized into specific building test cases. The detailed building models are implemented in Modelica using libraries that extend the IBPSA Modelica Library, including the Modelica Buildings Library and the IDEAS Library, and packaged as Functional Mockup Units for simulation within the standardized simulation environment. An API for this environment allows users to advance the simulation of the detailed building model with input from their control algorithms, receive data back from the detailed building model, and report key performance indicators, such as total energy use, operating cost, and thermal discomfort, after co-simulation is complete. Moreover, the BOPTEST-Gym interface enables the implementation of Reinforcement Learning (RL) algorithms in BOPTEST emulators out of the box.

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The focus of the workshop is to provide a hands-on introduction to the BOPTEST framework and its Gym interface, which aims to enable benchmarking and facilitate the development of advanced control strategies for buildings. The key objectives are to provide participants with:

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Program

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The workshop has a total duration of 3.5 hours and is divided into the following parts:

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  1. Presentation: BOPTEST motivation, goals, and approach (15m)
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  2. Exercise 1: Introductory hands-on tutorial
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    1. Introduce the Google Colab environment
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    2. Selecting a test case
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    3. Exploring the API
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    4. Develop a feedback controller for the test case
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    5. Add forecast to your feedback controller
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    6. Reporting results
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  3. Coffee break (10m)
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  4. Presentation 2: An example MPC controller in BOPTEST (10m)
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  5. Exercise 2: Learning BOPTEST-Gym, the Gym interface of BOPTEST (20m)
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  6. Exercise 3: Timed common exercise (1h10m)
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    1. Introduce exercise goals: optimize the controller from Exercise 1
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    2. Complete exercise, submit results, produce presentation reports
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    3. Volunteer participants present findings and group discussion
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  7. Wrap-Up and follow-up explanation (10m)
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Participant Prerequisites

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Participant Equipment Requirements

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Target Audience

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Building control developers interested in using simulation for control development and benchmarking. The public may be broad, from beginners to advanced control experts.

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Pages of Interest

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