rlcode/reinforcement-learning

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

Maintainers - Woongwon, Youngmoo, Hyeokreal, Uiryeong, Keon

From the basics to deep reinforcement learning, this repo provides easy-to-read code examples. One file for each algorithm. Please feel free to create a Pull Request, or open an issue!

1. Python 3.5
2. Tensorflow 1.0.0
3. Keras
4. numpy
5. pandas
6. matplot
7. pillow
8. Skimage
9. h5py

pip install -r requirements.txt

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

• Policy Iteration
• Value Iteration
• Monte Carlo
• SARSA
• Q-Learning
• Deep SARSA
• REINFORCE

CartPole - Applying deep reinforcement learning on basic Cartpole game.

• Deep Q Network
• Double Deep Q Network
• Policy Gradient
• Actor Critic (A2C)
• Asynchronous Advantage Actor Critic (A3C)

Atari - Mastering Atari games with Deep Reinforcement Learning

• Breakout - DQN, DDQN Dueling DDQN A3C
• Pong - Policy Gradient

OpenAI GYM - [WIP]

• Mountain Car - DQN