update path to aitraineree

.github/workflows/ci-test.yml

@@ -6,7 +6,7 @@ on:
       - $default-branch
   pull_request:
     paths:
-      - "ai_traineree/**.py"
+      - "aitraineree/**.py"
 
 jobs:
   build-n-test:

.gitignore

@@ -1,7 +1,7 @@
 data/
 build/
 dist/
-ai_traineree.egg-info/
+aitraineree.egg-info/
 
 videos/
 /gifs

README.md

@@ -26,9 +26,9 @@ That, and using PyTorch instead of Tensorflow or JAX.
 
 To get started with training your RL agent you need three things: an agent, an environment and a runner. Let's say you want to train a DQN agent on OpenAI CartPole-v1:
 ```python
-from ai_traineree.agents.dqn import DQNAgent
-from ai_traineree.runners.env_runner import EnvRunner
-from ai_traineree.tasks import GymTask
+from aitraineree.agents.dqn import DQNAgent
+from aitraineree.runners.env_runner import EnvRunner
+from aitraineree.tasks import GymTask
 
 task = GymTask('CartPole-v1')
 agent = DQNAgent(task.obs_space, task.action_space)
@@ -76,14 +76,14 @@ This is just a beginning and there will be more work on these interactions.
 
 | Short   | Progress                                      | Link                                                                                                           | Full name                                                 | Doc                                                                      |
 | ------- | --------------------------------------------- | -------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------- | ------------------------------------------------------------------------ |
-| DQN     | [Implemented](ai_traineree/agents/dqn.py)     | [DeepMind](https://deepmind.com/research/publications/human-level-control-through-deep-reinforcement-learning) | Deep Q-learning Network                                   | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#dqn)     |
-| DDPG    | [Implemented](ai_traineree/agents/ddpg.py)    | [arXiv](https://arxiv.org/abs/1509.02971)                                                                      | Deep Deterministic Policy Gradient                        | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#ddpg)    |
-| D4PG    | [Implemented](ai_traineree/agents/d4pg.py)    | [arXiv](https://arxiv.org/abs/1804.08617)                                                                      | Distributed Distributional Deterministic Policy Gradients | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#d4pg)    |
-| TD3     | [Implemented](ai_traineree/agents/td3.py)     | [arXiv](https://arxiv.org/abs/1802.09477)                                                                      | Twine Delayed Deep Deterministic policy gradient          | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#td3)     |
-| PPO     | [Implemented](ai_traineree/agents/ppo.py)     | [arXiv](https://arxiv.org/abs/1707.06347)                                                                      | Proximal Policy Optimization                              | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#ppo)     |
-| SAC     | [Implemented](ai_traineree/agents/sac.py)     | [arXiv](https://arxiv.org/abs/1801.01290)                                                                      | Soft Actor Critic                                         | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#sac)     |
+| DQN     | [Implemented](aitraineree/agents/dqn.py)     | [DeepMind](https://deepmind.com/research/publications/human-level-control-through-deep-reinforcement-learning) | Deep Q-learning Network                                   | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#dqn)     |
+| DDPG    | [Implemented](aitraineree/agents/ddpg.py)    | [arXiv](https://arxiv.org/abs/1509.02971)                                                                      | Deep Deterministic Policy Gradient                        | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#ddpg)    |
+| D4PG    | [Implemented](aitraineree/agents/d4pg.py)    | [arXiv](https://arxiv.org/abs/1804.08617)                                                                      | Distributed Distributional Deterministic Policy Gradients | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#d4pg)    |
+| TD3     | [Implemented](aitraineree/agents/td3.py)     | [arXiv](https://arxiv.org/abs/1802.09477)                                                                      | Twine Delayed Deep Deterministic policy gradient          | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#td3)     |
+| PPO     | [Implemented](aitraineree/agents/ppo.py)     | [arXiv](https://arxiv.org/abs/1707.06347)                                                                      | Proximal Policy Optimization                              | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#ppo)     |
+| SAC     | [Implemented](aitraineree/agents/sac.py)     | [arXiv](https://arxiv.org/abs/1801.01290)                                                                      | Soft Actor Critic                                         | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#sac)     |
 | TRPO    |                                               | [arXiv](https://arxiv.org/abs/1502.05477)                                                                      | Trust Region Policy Optimization                          |
-| RAINBOW | [Implemented](ai_traineree/agents/rainbow.py) | [arXiv](https://arxiv.org/abs/1710.02298)                                                                      | DQN with a few improvements                               | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#rainbow) |
+| RAINBOW | [Implemented](aitraineree/agents/rainbow.py) | [arXiv](https://arxiv.org/abs/1710.02298)                                                                      | DQN with a few improvements                               | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#rainbow) |
 
 ### Multi agents
 
@@ -92,8 +92,8 @@ However, that doesn't mean one can be used without the other.
 
 | Short  | Progress                                          | Link                                      | Full name              | Doc                                                                          |
 | ------ | ------------------------------------------------- | ----------------------------------------- | ---------------------- | ---------------------------------------------------------------------------- |
-| IQL    | [Implemented](ai_traineree/multi_agent/iql.py)    |                                           | Independent Q-Learners | [Doc](https://ai-traineree.readthedocs.io/en/latest/multi_agent.html#iql)    |
-| MADDPG | [Implemented](ai_traineree/multi_agent/maddpg.py) | [arXiv](https://arxiv.org/abs/1706.02275) | Multi agent DDPG       | [Doc](https://ai-traineree.readthedocs.io/en/latest/multi_agent.html#maddpg) |
+| IQL    | [Implemented](aitraineree/multi_agent/iql.py)    |                                           | Independent Q-Learners | [Doc](https://ai-traineree.readthedocs.io/en/latest/multi_agent.html#iql)    |
+| MADDPG | [Implemented](aitraineree/multi_agent/maddpg.py) | [arXiv](https://arxiv.org/abs/1706.02275) | Multi agent DDPG       | [Doc](https://ai-traineree.readthedocs.io/en/latest/multi_agent.html#maddpg) |
 
 ### Loggers
 

aitraineree/agents/__init__.py

@@ -2,7 +2,7 @@
 
 import torch
 
-from ai_traineree.types import AgentType
+from aitraineree.types import AgentType
 
 
 class AgentBase(AgentType):

aitraineree/agents/agent_factory.py

@@ -1,10 +1,10 @@
-from ai_traineree.agents import AgentBase
-from ai_traineree.agents.ddpg import DDPGAgent
-from ai_traineree.agents.dqn import DQNAgent
-from ai_traineree.agents.ppo import PPOAgent
-from ai_traineree.agents.rainbow import RainbowAgent
-from ai_traineree.agents.sac import SACAgent
-from ai_traineree.types import AgentState
+from aitraineree.agents import AgentBase
+from aitraineree.agents.ddpg import DDPGAgent
+from aitraineree.agents.dqn import DQNAgent
+from aitraineree.agents.ppo import PPOAgent
+from aitraineree.agents.rainbow import RainbowAgent
+from aitraineree.agents.sac import SACAgent
+from aitraineree.types import AgentState
 
 
 class AgentFactory:

aitraineree/agents/d3pg.py

@@ -6,18 +6,18 @@
 import torch.nn.functional as F
 from torch.optim import Adam
 
-from ai_traineree import DEVICE
-from ai_traineree.agents import AgentBase
-from ai_traineree.agents.agent_utils import hard_update, soft_update
-from ai_traineree.buffers.nstep import NStepBuffer
-from ai_traineree.buffers.per import PERBuffer
-from ai_traineree.loggers import DataLogger
-from ai_traineree.networks.bodies import ActorBody, CriticBody
-from ai_traineree.networks.heads import CategoricalNet
-from ai_traineree.policies import MultivariateGaussianPolicy, MultivariateGaussianPolicySimple
-from ai_traineree.types.dataspace import DataSpace
-from ai_traineree.types.experience import Experience
-from ai_traineree.utils import to_numbers_seq, to_tensor
+from aitraineree import DEVICE
+from aitraineree.agents import AgentBase
+from aitraineree.agents.agent_utils import hard_update, soft_update
+from aitraineree.buffers.nstep import NStepBuffer
+from aitraineree.buffers.per import PERBuffer
+from aitraineree.loggers import DataLogger
+from aitraineree.networks.bodies import ActorBody, CriticBody
+from aitraineree.networks.heads import CategoricalNet
+from aitraineree.policies import MultivariateGaussianPolicy, MultivariateGaussianPolicySimple
+from aitraineree.types.dataspace import DataSpace
+from aitraineree.types.experience import Experience
+from aitraineree.utils import to_numbers_seq, to_tensor
 
 
 class D3PGAgent(AgentBase):

aitraineree/agents/d4pg.py

@@ -11,18 +11,18 @@
 import torch.nn.functional as F
 from torch.optim import Adam
 
-from ai_traineree import DEVICE
-from ai_traineree.agents import AgentBase
-from ai_traineree.agents.agent_utils import hard_update, soft_update
-from ai_traineree.buffers.nstep import NStepBuffer
-from ai_traineree.buffers.per import PERBuffer
-from ai_traineree.loggers import DataLogger
-from ai_traineree.networks.bodies import ActorBody, CriticBody
-from ai_traineree.networks.heads import CategoricalNet
-from ai_traineree.policies import MultivariateGaussianPolicy, MultivariateGaussianPolicySimple
-from ai_traineree.types.dataspace import DataSpace
-from ai_traineree.types.experience import Experience
-from ai_traineree.utils import to_numbers_seq, to_tensor
+from aitraineree import DEVICE
+from aitraineree.agents import AgentBase
+from aitraineree.agents.agent_utils import hard_update, soft_update
+from aitraineree.buffers.nstep import NStepBuffer
+from aitraineree.buffers.per import PERBuffer
+from aitraineree.loggers import DataLogger
+from aitraineree.networks.bodies import ActorBody, CriticBody
+from aitraineree.networks.heads import CategoricalNet
+from aitraineree.policies import MultivariateGaussianPolicy, MultivariateGaussianPolicySimple
+from aitraineree.types.dataspace import DataSpace
+from aitraineree.types.experience import Experience
+from aitraineree.utils import to_numbers_seq, to_tensor
 
 
 class D4PGAgent(AgentBase):

aitraineree/agents/ddpg.py

@@ -8,18 +8,18 @@
 from torch.nn.functional import mse_loss
 from torch.optim import Adam
 
-from ai_traineree import DEVICE
-from ai_traineree.agents import AgentBase
-from ai_traineree.agents.agent_utils import hard_update, soft_update
-from ai_traineree.buffers.buffer_factory import BufferFactory
-from ai_traineree.buffers.replay import ReplayBuffer
-from ai_traineree.loggers import DataLogger
-from ai_traineree.networks.bodies import CriticBody, FcNet
-from ai_traineree.noise import GaussianNoise
-from ai_traineree.types import AgentState, BufferState, NetworkState
-from ai_traineree.types.dataspace import DataSpace
-from ai_traineree.types.experience import Experience
-from ai_traineree.utils import to_numbers_seq, to_tensor
+from aitraineree import DEVICE
+from aitraineree.agents import AgentBase
+from aitraineree.agents.agent_utils import hard_update, soft_update
+from aitraineree.buffers.buffer_factory import BufferFactory
+from aitraineree.buffers.replay import ReplayBuffer
+from aitraineree.loggers import DataLogger
+from aitraineree.networks.bodies import CriticBody, FcNet
+from aitraineree.noise import GaussianNoise
+from aitraineree.types import AgentState, BufferState, NetworkState
+from aitraineree.types.dataspace import DataSpace
+from aitraineree.types.experience import Experience
+from aitraineree.utils import to_numbers_seq, to_tensor
 
 
 class DDPGAgent(AgentBase):

aitraineree/agents/dqn.py

@@ -6,18 +6,18 @@
 import torch.nn.functional as F
 import torch.optim as optim
 
-from ai_traineree import DEVICE
-from ai_traineree.agents import AgentBase
-from ai_traineree.agents.agent_utils import soft_update
-from ai_traineree.buffers.buffer_factory import BufferFactory
-from ai_traineree.buffers.nstep import NStepBuffer
-from ai_traineree.buffers.per import PERBuffer
-from ai_traineree.loggers import DataLogger
-from ai_traineree.networks import NetworkType, NetworkTypeClass
-from ai_traineree.networks.heads import DuelingNet
-from ai_traineree.types import AgentState, BufferState, DataSpace, NetworkState
-from ai_traineree.types.experience import Experience
-from ai_traineree.utils import to_numbers_seq, to_tensor
+from aitraineree import DEVICE
+from aitraineree.agents import AgentBase
+from aitraineree.agents.agent_utils import soft_update
+from aitraineree.buffers.buffer_factory import BufferFactory
+from aitraineree.buffers.nstep import NStepBuffer
+from aitraineree.buffers.per import PERBuffer
+from aitraineree.loggers import DataLogger
+from aitraineree.networks import NetworkType, NetworkTypeClass
+from aitraineree.networks.heads import DuelingNet
+from aitraineree.types import AgentState, BufferState, DataSpace, NetworkState
+from aitraineree.types.experience import Experience
+from aitraineree.utils import to_numbers_seq, to_tensor
 
 
 class DQNAgent(AgentBase):

aitraineree/agents/dummy.py

@@ -1,8 +1,8 @@
 import numpy as np
 
-from ai_traineree.agents import AgentBase
-from ai_traineree.types import DataSpace
-from ai_traineree.types.experience import Experience
+from aitraineree.agents import AgentBase
+from aitraineree.types import DataSpace
+from aitraineree.types.experience import Experience
 
 
 class DummyAgent(AgentBase):

aitraineree/agents/ppo.py

@@ -7,18 +7,18 @@
 import torch.nn.functional as F
 import torch.optim as optim
 
-from ai_traineree import DEVICE
-from ai_traineree.agents import AgentBase
-from ai_traineree.agents.agent_utils import compute_gae, normalize, revert_norm_returns
-from ai_traineree.buffers.buffer_factory import BufferFactory
-from ai_traineree.buffers.rollout import RolloutBuffer
-from ai_traineree.loggers import DataLogger
-from ai_traineree.networks.bodies import ActorBody
-from ai_traineree.policies import MultivariateGaussianPolicy, MultivariateGaussianPolicySimple
-from ai_traineree.types import ActionType, AgentState, BufferState, NetworkState
-from ai_traineree.types.dataspace import DataSpace
-from ai_traineree.types.experience import Experience
-from ai_traineree.utils import to_numbers_seq, to_tensor
+from aitraineree import DEVICE
+from aitraineree.agents import AgentBase
+from aitraineree.agents.agent_utils import compute_gae, normalize, revert_norm_returns
+from aitraineree.buffers.buffer_factory import BufferFactory
+from aitraineree.buffers.rollout import RolloutBuffer
+from aitraineree.loggers import DataLogger
+from aitraineree.networks.bodies import ActorBody
+from aitraineree.policies import MultivariateGaussianPolicy, MultivariateGaussianPolicySimple
+from aitraineree.types import ActionType, AgentState, BufferState, NetworkState
+from aitraineree.types.dataspace import DataSpace
+from aitraineree.types.experience import Experience
+from aitraineree.utils import to_numbers_seq, to_tensor
 
 
 class PPOAgent(AgentBase):

aitraineree/agents/rainbow.py

@@ -5,18 +5,18 @@
 import torch.nn as nn
 import torch.optim as optim
 
-from ai_traineree import DEVICE
-from ai_traineree.agents import AgentBase
-from ai_traineree.agents.agent_utils import soft_update
-from ai_traineree.buffers.buffer_factory import BufferFactory
-from ai_traineree.buffers.nstep import NStepBuffer
-from ai_traineree.buffers.per import PERBuffer
-from ai_traineree.loggers import DataLogger
-from ai_traineree.networks.heads import RainbowNet
-from ai_traineree.types import AgentState, BufferState, NetworkState
-from ai_traineree.types.dataspace import DataSpace
-from ai_traineree.types.experience import Experience
-from ai_traineree.utils import to_numbers_seq, to_tensor
+from aitraineree import DEVICE
+from aitraineree.agents import AgentBase
+from aitraineree.agents.agent_utils import soft_update
+from aitraineree.buffers.buffer_factory import BufferFactory
+from aitraineree.buffers.nstep import NStepBuffer
+from aitraineree.buffers.per import PERBuffer
+from aitraineree.loggers import DataLogger
+from aitraineree.networks.heads import RainbowNet
+from aitraineree.types import AgentState, BufferState, NetworkState
+from aitraineree.types.dataspace import DataSpace
+from aitraineree.types.experience import Experience
+from aitraineree.utils import to_numbers_seq, to_tensor
 
 
 class RainbowAgent(AgentBase):

aitraineree/agents/sac.py

@@ -7,19 +7,19 @@
 import torch.nn as nn
 from torch import Tensor, optim
 
-from ai_traineree import DEVICE
-from ai_traineree.agents import AgentBase
-from ai_traineree.agents.agent_utils import hard_update, soft_update
-from ai_traineree.buffers import PERBuffer
-from ai_traineree.buffers.buffer_factory import BufferFactory
-from ai_traineree.loggers import DataLogger
-from ai_traineree.networks.bodies import ActorBody, CriticBody
-from ai_traineree.networks.heads import DoubleCritic
-from ai_traineree.policies import GaussianPolicy, MultivariateGaussianPolicySimple
-from ai_traineree.types.dataspace import DataSpace
-from ai_traineree.types.experience import Experience
-from ai_traineree.types.state import AgentState, BufferState, NetworkState
-from ai_traineree.utils import to_numbers_seq, to_tensor
+from aitraineree import DEVICE
+from aitraineree.agents import AgentBase
+from aitraineree.agents.agent_utils import hard_update, soft_update
+from aitraineree.buffers import PERBuffer
+from aitraineree.buffers.buffer_factory import BufferFactory
+from aitraineree.loggers import DataLogger
+from aitraineree.networks.bodies import ActorBody, CriticBody
+from aitraineree.networks.heads import DoubleCritic
+from aitraineree.policies import GaussianPolicy, MultivariateGaussianPolicySimple
+from aitraineree.types.dataspace import DataSpace
+from aitraineree.types.experience import Experience
+from aitraineree.types.state import AgentState, BufferState, NetworkState
+from aitraineree.utils import to_numbers_seq, to_tensor
 
 
 class SACAgent(AgentBase):

aitraineree/agents/td3.py

@@ -5,17 +5,17 @@
 from torch.nn.functional import mse_loss
 from torch.optim import Adam
 
-from ai_traineree import DEVICE
-from ai_traineree.agents import AgentBase
-from ai_traineree.agents.agent_utils import hard_update, soft_update
-from ai_traineree.buffers.replay import ReplayBuffer
-from ai_traineree.loggers import DataLogger
-from ai_traineree.networks.bodies import ActorBody, CriticBody
-from ai_traineree.networks.heads import DoubleCritic
-from ai_traineree.noise import GaussianNoise
-from ai_traineree.types.dataspace import DataSpace
-from ai_traineree.types.experience import Experience
-from ai_traineree.utils import to_numbers_seq, to_tensor
+from aitraineree import DEVICE
+from aitraineree.agents import AgentBase
+from aitraineree.agents.agent_utils import hard_update, soft_update
+from aitraineree.buffers.replay import ReplayBuffer
+from aitraineree.loggers import DataLogger
+from aitraineree.networks.bodies import ActorBody, CriticBody
+from aitraineree.networks.heads import DoubleCritic
+from aitraineree.noise import GaussianNoise
+from aitraineree.types.dataspace import DataSpace
+from aitraineree.types.experience import Experience
+from aitraineree.utils import to_numbers_seq, to_tensor
 
 
 class TD3Agent(AgentBase):