This Repository is Reinforcement Learning Agent FrameWork

tensorflow_RL-master.zip

This repository is designed to provide an easy demo reinforcement learning framework for those studying deep reinforcement learning.

This framework is based on a tensorflow. And the basic model is implemented in example_model directory. If you want to use your own model, please refer provided model in example_model directory

We provide a tutorial to train the agent for the environment, and tutorials by action and input shape are provided as follows.

Environment

Continuous Action MLP - bipedalwalker, pendulum
Discrete Action MLP - LunarLander
Discrete Action CNN - Breakout

Algorithms

Continuous Action MLP - DDPG, TD3, PPO, PPO2
Discrete Action MLP - Vanilla PG, A2C, PPO, DQN, QRDQN, IQN
Discrete Action CNN - Vanilla PG, A2C, PPO, DQN, QRDQN, IQN

Our tutorial is being done in the gym environment provided by openai and you need to install the openai gym and box2d to run the tutorial code.

Installation

from git repository

https://github.com/RLOpensource/tensorflow_RL
pip install .

cpu version

pip install tensorflow-rl[tf-cpu]

gpu version

pip install tensorflow-rl[tf-gpu]

If you install this repository by only

pip install tensorflow-rl

tensorflow is not installed

Requirements

tensorflow
box2d
gym
numpy
tensorboardX

Implemented

•  Vanilla Policy Gradient
•  Advantage Actor Critic
•  Proximal Policy Optimization
•  Deep Deterministic Policy Gradient
•  Value based Reinforcement Learning
•  Soft Actor Critic
•  LSTM train Algorithm

Demonstration

1. Continuous Action BipedalWalker

• Script : bipedalwalker_td3.py, bipedalwalker_ddpg.py, bipedalwalker_ppo.py, bipedalwalker_ppo2.py
• Environment : BipedalWalker-v2
• Orange : td3, Blue: ddpg, SkyBlue: ppo, Pink: ppo2
• Episode : 600
• Image : td3

BipedalWalker

2. Continuous Action Pendulum

• Script : pendulum_td3.py, pendulum_ddpg.py
• Environment : Pendulum-v0
• Orange : ddpg, Blue: td3
• Episode : 300
• Image : td3

Pendulum

3. Discrete Action CNN Breakout

• Script : breakout_rollout_a2c.py, breakout_rollout_ppo.py, breakout_rollout_vpg.py
• Environment : BreakoutDeterministic-v4 with Multi-processing
• Blue : ppo, Orange : a2c, Red : vpg
• Episode : 600
• Image : PPO

Breakout

4. Discrete Action MLP LunarLander

• Script : lunarLander_rollout_a2c.py, lunarLander_rollout_ppo.py, lunarLander_rollout_vpg.py
• Environment : LunarLander-v2 with Multi-processing
• Blue : ppo, Orange : a2c, Red : vpg
• Episode : 350
• Image : PPO

LunarLander

5. Value Based Reinforcement Learning with CNN

• Script : breakout_value_dqn.py, breakout_value_qrdqn.py, breakout_value_iqn.py
• Environment : BreakoutDeterministic-v4 with Multi-processing
• Green : IQN, Blue : QRDQN, Pink : DQN
• Episode : 280
• Image : IQN

Breakout

 

6. Value Based Reinforcement Learning with MLP

• Script : lunarLander_value_dqn.py, lunarLander_value_qrdqn.py, lunarLander_value_iqn.py
• Environment : LunarLander-v2 with Multi-processing
• Orange : IQN, Blue : QRDQN, Red : DQN
• Episode : 250
• Image : IQN

Breakout

 

7. Discrete Action CNN LSTM Breakout inspired from drqn

• Script : breakout_rollout_ppo_1stack_lstm.py, breakout_rollout_ppo_1stack.py
• Environment : BreakoutDeterministic-v4 with Multi-processing
• Orange : PPOLSTM, Blue : PPO-1stack
• Episode : 1000
• Image : PPOLSTM

Breakout

 

Member

• https://github.com/chagmgang
• https://github.com/SunandBean
• https://github.com/TinkTheBoush

License

We do not have the copyright to this repository.

Please 'just' use these code and just 'refer' the url of repository in any form.

MIT License

Reference

[1] mario_rl

[2] Proximal Policy Optimization

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어린이용이며, 설치가 필요없는 브라우저 게임입니다.
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[3] Efficient Parallel Methods for Deep Reinforcement Learning

[4] High-Dimensional Continuous Control Using Generalized Advantage Estimation

[5] Asynchronous Methods for Deep Reinforcement Learning

[6] Continuous Control With Deep Reinforcement Learning

[7] Vanilla Policy Gradient

[8] Deep Recurrent Q-Learning for Partially Observable MDPs

[9] Playing Atari with Deep Reinforcement Learning

[10] Distributional Reinforcement Learning with Quantile Regression

[11] Implicit Quantile Networks for Distributional Reinforcement Learning

[12] OpenAI Spinningup

[13] Reinforcement Learning Korea PG Travel

[14] Medipixel Reinforcement Learning Repository

Please fork this repository and contribute to strengthen the tensorflow reinforcement learning ecosystem

Support us in any form. Thank you

Content us to chagmgang@gmail.com

[출처] https://github.com/RLOpensource/tensorflow_RL