Run_Godot_experiments.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "waiting for remote GODOT connection on port 12305\n",
      "connection established\n",
      "1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.Experiment Concluded\n",
      "close message sent\n"
     ]
    }
   ],
   "source": [
    "from GodotExperimentWrapper import GodotExperimentWrapper\n",
    "import pandas as pd\n",
    "import json\n",
    "\n",
    "\n",
    "config_dict =   { 'EnvConfig' : {\n",
    "                            'task': 'b_ace_v1',\n",
    "                            'env_path': 'BVR_AirCombat/bin/B_ACE_v4.exe',\n",
    "                            'renderize': 1,\n",
    "                            'experiment_mode'  : 1,\n",
    "                            'parallel_envs': 5,\n",
    "                            'seed': 1,\n",
    "                            'port': 12500,\n",
    "                            'action_repeat': 20,\n",
    "                            'speedup': 2000,\n",
    "                            'action_type': 'Low_Level_Continuous' ,                        \n",
    "                            'full_observation': 1,\n",
    "                            'actions_2d': 0\n",
    "                            },\n",
    "                        'SimConfig' : {                        \n",
    "                            'num_allies': 1,\n",
    "                            'num_enemies': 1,\n",
    "                            'enemies_behavior': 'baseline1',\n",
    "                            'agents_behavior' : 'baseline1',\n",
    "                            'agents_config' : { \n",
    "                                'blue_agents': \n",
    "                                    {                        \n",
    "                                        'init_position': {'x': 0.0, 'y': 25000.0,'z': 30.0},\n",
    "                                        'offset_pos': {\t'x': 0.0, 'y': 0.0, 'z': 0.0},\n",
    "                                        'init_hdg': 0.0,                        \n",
    "                                        'target_position': {'x': 0.0,'y': 25000.0,'z': -30.0},\n",
    "                                        'rnd_offset_range':{'x': 15.0,'y': 10000.0,'z': 5.0},\n",
    "                                        #'rnd_offset_range':{'x': 0.0,'y': 0.0,'z': 0.0},\n",
    "                                        'rnd_shot_dist_var': 0.1 \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
    "                                    },                            \n",
    "                                'red_agents':\n",
    "                                    { \n",
    "                                        'init_position': {'x': 0.0,'y': 25000.0,'z': -30.0},\n",
    "                                        'offset_pos': {'x': 0.0,'y': 0.0,'z': 0.0},\n",
    "                                        'init_hdg' : 180.0,                        \n",
    "                                        'target_position': {'x': 0.0,'y': 25000.0,'z': 30.0},\n",
    "                                        'rnd_offset_range':{'x': 15.0,'y': 10000.0,'z': 5.0},\n",
    "                                        #'rnd_offset_range':{'x': 0.0,'y': 0.0,'z': 0.0},\n",
    "                                        'rnd_shot_dist_var': 0.1\t\t\t\t\t\n",
    "                                    }   \n",
    "                            } \n",
    "                            \n",
    "                        },\n",
    "                        'ExperimentConfig' : {\n",
    "                            'runs_per_case': 30,\n",
    "                            'cases': [\n",
    "                                {'num_enemies': 4, 'num_allies': 4},\n",
    "                                {'num_enemies': 4, 'num_allies': 4},\n",
    "                                {'num_enemies': 4, 'num_allies': 4},\n",
    "                                {'num_enemies': 4, 'num_allies': 4},\n",
    "                                {'num_enemies': 4, 'num_allies': 4}                                                                   \n",
    "                                ]\n",
    "                            }\n",
    "                    }\n",
    "        \n",
    "\n",
    "# Create the GodotExperimentWrapper\n",
    "env = GodotExperimentWrapper(config_dict)\n",
    "\n",
    "# Run the experiment\n",
    "results = env.watch_experiment()\n",
    "env.close()\n",
    "\n",
    "# Save the experiment results to a file\n",
    "with open('experiment_results.json' , \"w\") as file:\n",
    "    json.dump(results, file, indent=4)\n",
    "\n",
    "# Flatten the list of lists into a single list of dictionaries\n",
    "flat_data = [item for sublist in results for item in sublist]\n",
    "# Create the DataFrame\n",
    "df = pd.DataFrame(flat_data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "356\n",
      "410\n"
     ]
    }
   ],
   "source": [
    "print(sum(df.blues_killed))\n",
    "print(sum(df.reds_killed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assume the data is stored in a CSV file named 'data.csv'\n",
    "#df = pd.read_csv('data.csv', sep='\\s+')\n",
    "\n",
    "# Group the data by 'env_id' and calculate the mean of 'blues_killed' and 'reds_killed' for each group\n",
    "grouped_data = df.groupby('env_id')[['blues_killed', 'reds_killed']].sum()\n",
    "\n",
    "# Create a grouped bar plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "grouped_data.plot(kind='bar', width=0.8)\n",
    "plt.xlabel('Environment ID')\n",
    "plt.ylabel('Average Count')\n",
    "plt.title('Average Count of Blues and Reds Killed by Environment')\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend(['Blues Killed', 'Reds Killed'])\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}