v0.0.2 release

README.md

@@ -4,34 +4,36 @@
     <br>
 <p>
 
-[![pypi](https://img.shields.io/badge/pypi%20package-v0.0.1-blue)](https://pypi.org/project/genrl/)
+[![pypi](https://img.shields.io/badge/pypi%20package-v0.0.2-blue)](https://pypi.org/project/genrl/)
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-![Lint, Test, Code Coverage](https://github.com/SforAiDl/genrl/workflows/Lint,%20Test,%20Code%20Coverage/badge.svg)
+[![Lint, Test, Code Coverage](https://github.com/SforAiDl/genrl/workflows/Lint,%20Test,%20Code%20Coverage/badge.svg)
+[![Slack - Chat](https://img.shields.io/badge/Slack-Chat-blueviolet)](https://join.slack.com/t/genrlworkspace/shared_invite/zt-gwlgnymd-Pw3TYC~0XDLy6VQDml22zg)
 
 ---
 
 [![](https://sourcerer.io/fame/Sharad24/Sharad24/genrl/images/0)](https://sourcerer.io/fame/Sharad24/Sharad24/genrl/links/0)[![](https://sourcerer.io/fame/Sharad24/Sharad24/genrl/images/1)](https://sourcerer.io/fame/Sharad24/Sharad24/genrl/links/1)[![](https://sourcerer.io/fame/Sharad24/Sharad24/genrl/images/2)](https://sourcerer.io/fame/Sharad24/Sharad24/genrl/links/2)[![](https://sourcerer.io/fame/Sharad24/Sharad24/genrl/images/3)](https://sourcerer.io/fame/Sharad24/Sharad24/genrl/links/3)[![](https://sourcerer.io/fame/Sharad24/Sharad24/genrl/images/4)](https://sourcerer.io/fame/Sharad24/Sharad24/genrl/links/4)[![](https://sourcerer.io/fame/Sharad24/Sharad24/genrl/images/5)](https://sourcerer.io/fame/Sharad24/Sharad24/genrl/links/5)[![](https://sourcerer.io/fame/Sharad24/Sharad24/genrl/images/6)](https://sourcerer.io/fame/Sharad24/Sharad24/genrl/links/6)[![](https://sourcerer.io/fame/Sharad24/Sharad24/genrl/images/7)](https://sourcerer.io/fame/Sharad24/Sharad24/genrl/links/7)
 
 ---
 
-**GenRL is a PyTorch reinforcement learning library centered around reproducible and generalizable algorithm implementations.** 
+**GenRL is a PyTorch reinforcement learning library centered around reproducible, generalizable algorithm implementations and improving accessibility in Reinforcement Learning** 
 
 Reinforcement learning research is moving faster than ever before. In order to keep up with the growing trend and ensure that RL research remains reproducible, GenRL aims to aid faster paper reproduction and benchmarking by providing the following main features:
 
 - **PyTorch-first**: Modular, Extensible and Idiomatic Python
+- **Tutorials and Example**: 20+ Tutorials from basic RL to SOTA Deep RL algorithm (with explanations)!
 - **Unified Trainer and Logging class**: code reusability and high-level UI
 - **Ready-made algorithm implementations**: ready-made implementations of popular RL algorithms.
 - **Faster Benchmarking**: automated hyperparameter tuning, environment implementations etc.
 
 By integrating these features into GenRL, we aim to eventually support **any new algorithm implementation in less than 100 lines**.
 
-**If you're interested in contributing, feel free to go through the issues and open PRs for code, docs, tests etc. In case of any questions, please check out the [Contributing Guidelines](https://github.com/SforAiDl/genrl/wiki/Contributing-Guidelines)**
+**If you're interested in contributing, feel free to go through the issues and open PRs for code, docs, tests etc. In case of any questions, please check out the [Contributing Guidelines](CONTRIBUTING.md)**
 
 
 ## Installation
@@ -55,10 +57,9 @@ To train a Soft Actor-Critic model from scratch on the `Pendulum-v0` gym environ
 ```python
 import gym
 
-from genrl import SAC, QLearning
-from genrl.classical.common import Trainer
-from genrl.deep.common import OffPolicyTrainer
+from genrl.agents import SAC, QLearning
 from genrl.environments import VectorEnv
+from genrl.trainers import ClassicalTrainer, OffPolicyTrainer
 
 env = VectorEnv("Pendulum-v0")
 agent = SAC('mlp', env)
@@ -69,13 +70,30 @@ trainer.train()
 To train a Tabular Dyna-Q model from scratch on the `FrozenLake-v0` gym environment and plot rewards:
 ```python
 
+
 env = gym.make("FrozenLake-v0")
 agent = QLearning(env)
-trainer = Trainer(agent, env, mode="dyna", model="tabular", n_episodes=10000)
+trainer = ClassicalTrainer(agent, env, mode="dyna", model="tabular", n_episodes=10000)
 episode_rewards = trainer.train()
 trainer.plot(episode_rewards)
 ```
 
+## Tutorials
+- [Multi Armed Bandits](https://genrl.readthedocs.io/en/latest/usage/tutorials/bandit/bandit_overview.html)
+    - [Upper Confidence Bound](https://genrl.readthedocs.io/en/latest/usage/tutorials/bandit/ucb.html)
+    - [Thompson Sampling](https://genrl.readthedocs.io/en/latest/usage/tutorials/bandit/thompson_sampling.html)
+    - [Bayesian](https://genrl.readthedocs.io/en/latest/usage/tutorials/bandit/bayesian.html)
+    - [Softmax Action Selection](https://genrl.readthedocs.io/en/latest/usage/tutorials/bandit/gradients.html)
+- [Contextual Bandits](https://genrl.readthedocs.io/en/latest/usage/tutorials/bandit/contextual_overview.html)
+    - [Linear Posterior Inference](https://genrl.readthedocs.io/en/latest/usage/tutorials/bandit/linpos.html)
+    - [Variational Inference](https://genrl.readthedocs.io/en/latest/usage/tutorials/bandit/variational.html)
+    - [https://genrl.readthedocs.io/en/latest/usage/tutorials/bandit/bootstrap.html](Bootstrap)
+    - [Parameter Noise Sampling](https://genrl.readthedocs.io/en/latest/usage/tutorials/bandit/noise.html)
+- [Deep Reinforcement Learning Background](https://genrl.readthedocs.io/en/latest/usage/tutorials/Deep/Background.html)
+    - [Vanilla Policy Gradients](https://genrl.readthedocs.io/en/latest/usage/tutorials/Deep/VPG.html)
+    - [Advantage Actor Critic](https://genrl.readthedocs.io/en/latest/usage/tutorials/Deep/A2C.html)
+    - [Proximal Policy Optimization](https://genrl.readthedocs.io/en/latest/usage/tutorials/Deep/PPO.html)
+
 ## Algorithms
 
 ### Deep RL

docs/source/api/agents/genrl.agents.classical.sarsa.rst

@@ -8,4 +8,3 @@ genrl.agents.classical.sarsa.sarsa module
    :members:
    :undoc-members:
    :show-inheritance:
-

docs/source/api/agents/genrl.agents.deep.ppo1.rst

@@ -8,4 +8,3 @@ genrl.agents.deep.ppo1.ppo1 module
    :members:
    :undoc-members:
    :show-inheritance:
-

docs/source/api/agents/genrl.agents.deep.sac.rst

@@ -9,4 +9,3 @@ genrl.agents.deep.sac.sac module
    :members:
    :undoc-members:
    :show-inheritance:
-

docs/source/api/agents/genrl.agents.deep.td3.rst

@@ -8,4 +8,3 @@ genrl.agents.deep.td3.td3 module
    :members:
    :undoc-members:
    :show-inheritance:
-

docs/source/api/agents/genrl.agents.deep.vpg.rst

@@ -9,4 +9,3 @@ genrl.agents.deep.vpg.vpg module
    :members:
    :undoc-members:
    :show-inheritance:
-

docs/source/usage/tutorials/Classical/Q_Learning.rst

@@ -80,4 +80,3 @@ Great so far so good! Now moving towards the training process it is just calling
 
 
 That's it! You have successfully trained a Q-Learning agent. You can now go ahead and play with your own environments using GenRL! 
-

docs/source/usage/tutorials/Classical/Sarsa.rst

@@ -67,4 +67,4 @@ Great so far so good! Now moving towards the training process it is just calling
     trainer.train()
     trainer.evaluate()
 
-That's it! You have successfully trained a SARSA agent. You can now go ahead and play with your own environments using GenRL!
+That's it! You have successfully trained a SARSA agent. You can now go ahead and play with your own environments using GenRL!

genrl/__init__.py

@@ -0,0 +1 @@
+version = "0.0.2"

genrl/agents/bandits/contextual/common/base_model.py

@@ -2,8 +2,8 @@
 from typing import Dict
 
 import torch
-import torch.nn as nn
-import torch.nn.functional as F
+from torch import nn as nn
+from torch.nn import functional as F
 
 from genrl.agents.bandits.contextual.common.transition import TransitionDB
 

genrl/agents/bandits/contextual/common/bayesian.py

@@ -1,8 +1,8 @@
 from typing import Dict, Optional, Tuple
 
 import torch
-import torch.nn as nn
-import torch.nn.functional as F
+from torch import nn as nn
+from torch.nn import functional as F
 
 from genrl.agents.bandits.contextual.common.base_model import Model
 from genrl.agents.bandits.contextual.common.transition import TransitionDB

genrl/agents/bandits/contextual/common/neural.py

@@ -1,8 +1,8 @@
 from typing import Dict
 
 import torch
-import torch.nn as nn
-import torch.nn.functional as F
+from torch import nn as nn
+from torch.nn import functional as F
 
 from genrl.agents.bandits.contextual.common.base_model import Model
 from genrl.agents.bandits.contextual.common.transition import TransitionDB

genrl/agents/deep/a2c/a2c.py

@@ -3,8 +3,8 @@
 import gym
 import numpy as np
 import torch
-import torch.nn.functional as F
-import torch.optim as opt
+from torch import optim as opt
+from torch.nn import functional as F
 
 from genrl.agents.deep.base import OnPolicyAgent
 from genrl.utils import get_env_properties, get_model, safe_mean

genrl/agents/deep/ddpg/ddpg.py

@@ -2,7 +2,7 @@
 from typing import Any, Dict
 
 import numpy as np
-import torch.optim as opt
+from torch import optim as opt
 
 from genrl.agents import OffPolicyAgentAC
 from genrl.core import ActionNoise

genrl/agents/deep/dqn/base.py

@@ -3,7 +3,7 @@
 
 import numpy as np
 import torch
-import torch.optim as opt
+from torch import optim as opt
 
 from genrl.agents import OffPolicyAgent
 from genrl.utils import get_env_properties, get_model, safe_mean

genrl/agents/deep/ppo1/ppo1.py

@@ -3,8 +3,8 @@
 import gym
 import numpy as np
 import torch
-import torch.nn as nn
-import torch.optim as opt
+from torch import nn as nn
+from torch import optim as opt
 
 from genrl.agents import OnPolicyAgent
 from genrl.utils import get_env_properties, get_model, safe_mean

genrl/agents/deep/sac/sac.py

@@ -3,7 +3,7 @@
 
 import numpy as np
 import torch
-import torch.optim as opt
+from torch import optim as opt
 
 from genrl.agents import OffPolicyAgentAC
 from genrl.utils import get_env_properties, get_model, safe_mean

genrl/agents/deep/vpg/vpg.py

@@ -3,7 +3,7 @@
 import gym
 import numpy as np
 import torch
-import torch.optim as opt
+from torch import optim as opt
 
 from genrl.agents import OnPolicyAgent
 from genrl.utils import get_env_properties, get_model, safe_mean

genrl/core/actor_critic.py

@@ -2,8 +2,8 @@
 
 import numpy as np
 import torch
-import torch.nn as nn
 from gym import spaces
+from torch import nn as nn
 from torch.distributions import Categorical, Normal
 
 from genrl.core.base import BaseActorCritic

genrl/core/bandit.py

@@ -2,7 +2,7 @@
 from typing import List, Tuple, Union
 
 import torch
-import torch.nn.functional as F
+from torch.nn import functional as F
 
 
 class Bandit(ABC):

genrl/core/base.py

@@ -1,7 +1,7 @@
 from typing import Optional, Tuple
 
 import torch
-import torch.nn as nn
+from torch import nn as nn
 from torch.distributions import Categorical, Normal
 
 

genrl/core/noise.py

@@ -2,7 +2,7 @@
 
 import numpy as np
 import torch
-import torch.nn as nn
+from torch import nn as nn
 
 
 class ActionNoise(ABC):

genrl/utils/utils.py

@@ -4,7 +4,7 @@
 import gym
 import numpy as np
 import torch
-import torch.nn as nn
+from torch import nn as nn
 
 from genrl.core.base import BaseActorCritic, BasePolicy, BaseValue
 from genrl.core.noise import NoisyLinear