https://github.com/rlcode/reinforcement-learning

Minimal and Clean Reinforcement Learning Examples
https://github.com/rlcode/reinforcement-learning

a3c actor-critic deep-learning deep-q-network deep-reinforcement-learning dqn machine-learning policy-gradient reinforcement-learning

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Minimal and Clean Reinforcement Learning Examples

• Host: GitHub
• URL: https://github.com/rlcode/reinforcement-learning
• Owner: rlcode
• License: mit
• Created: 2017-01-13T09:30:09.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2023-03-24T21:57:34.000Z (over 1 year ago)
• Last Synced: 2024-12-13T07:04:37.860Z (9 days ago)
• Topics: a3c, actor-critic, deep-learning, deep-q-network, deep-reinforcement-learning, dqn, machine-learning, policy-gradient, reinforcement-learning
• Language: Python
• Homepage:
• Size: 60.2 MB
• Stars: 3,432
• Watchers: 129
• Forks: 734
• Open Issues: 36
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-machine-learning-resources - **[Code Collection - learning?style=social) (Table of Contents)

README

        


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> Minimal and clean examples of reinforcement learning algorithms presented by [RLCode](https://rlcode.github.io) team. [[한국어]](https://github.com/rlcode/reinforcement-learning-kr)

>

> Maintainers - [Woongwon](https://github.com/dnddnjs), [Youngmoo](https://github.com/zzing0907), [Hyeokreal](https://github.com/Hyeokreal), [Uiryeong](https://github.com/wooridle), [Keon](https://github.com/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](https://github.com/rlcode/reinforcement-learning/pulls), or open an [issue](https://github.com/rlcode/reinforcement-learning/issues)!

## Dependencies

1. Python 3.5

2. Tensorflow 1.0.0

3. Keras

4. numpy

5. pandas

6. matplot

7. pillow

8. Skimage

9. h5py

### Install Requirements

```

pip install -r requirements.txt

```

## Table of Contents

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

- [Policy Iteration](./1-grid-world/1-policy-iteration)

- [Value Iteration](./1-grid-world/2-value-iteration)

- [Monte Carlo](./1-grid-world/3-monte-carlo)

- [SARSA](./1-grid-world/4-sarsa)

- [Q-Learning](./1-grid-world/5-q-learning)

- [Deep SARSA](./1-grid-world/6-deep-sarsa)

- [REINFORCE](./1-grid-world/7-reinforce)

**CartPole** - Applying deep reinforcement learning on basic Cartpole game.

- [Deep Q Network](./2-cartpole/1-dqn)

- [Double Deep Q Network](./2-cartpole/2-double-dqn)

- [Policy Gradient](./2-cartpole/3-reinforce)

- [Actor Critic (A2C)](./2-cartpole/4-actor-critic)

- [Asynchronous Advantage Actor Critic (A3C)](./2-cartpole/5-a3c)

**Atari** - Mastering Atari games with Deep Reinforcement Learning

- **Breakout** - [DQN](./3-atari/1-breakout/breakout_dqn.py), [DDQN](./3-atari/1-breakout/breakout_ddqn.py) [Dueling DDQN](./3-atari/1-breakout/breakout_ddqn.py) [A3C](./3-atari/1-breakout/breakout_a3c.py)

- **Pong** - [Policy Gradient](./3-atari/2-pong/pong_reinforce.py)

**OpenAI GYM** - [WIP]

- Mountain Car - [DQN](./4-gym/1-mountaincar)