namghiwook / reinforcement-learning

Minimal and Clean Reinforcement Learning Examples

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Minimal and clean examples of reinforcement learning algorithms presented by RLCode team. [한국어]

Maintainers - Woongwon, Youngmoo, Hyeokreal, Uiryeong, Keon

From the most basic algorithms to the more recent ones categorized as 'deep reinforcement learning', the examples are easy to read with comments. Please feel free to create a Pull Request, or open an issue!

Dependencies

1. Python 3.5
2. Tensorflow 1.0.0
3. Keras
4. numpy
5. pandas
6. matplot

Install Requirements

pip install -r requirements.txt

Table of Contents

Code 1 - Mastering the basics of reinforcement learning in the simplified world called "Grid World"

• Policy Iteration
• Value Iteration
• Monte Carlo
• SARSA
• Q-Learning
• Deep Q Network
• Policy Gradient

Code 2 - Applying deep reinforcement learning on basic Cartpole game.

• Deep Q Network
• Double Deep Q Network
• Dueling Deep Q Network
• Policy Gradient
• Actor Critic
• Asynchronous Advantage Actor Critic (A3C) - WIP

Code 3 - Mastering Atari games with Deep Reinforcement Learning

• Breakout - DQN, PG, A3C
• Pong - DQN, PG, A3C