Skip to content
Minimal and Clean Reinforcement Learning Examples
Branch: master
Clone or download

Latest commit

Latest commit 2fe6984 Nov 28, 2017


Type Name Latest commit message Commit time
Failed to load latest commit information.
1-grid-world fix error q-learning learning function Jul 8, 2017
2-cartpole Add DQN with PER Nov 28, 2017
3-atari add comment on use of categorical_crossentropy Jul 13, 2017
4-gym/1-mountaincar update readme and fix more folder and file names May 30, 2017
images dd Apr 17, 2017
wiki Update May 21, 2017
.gitignore update repository May 15, 2017
LICENSE Create LICENSE May 8, 2017 Update Jul 9, 2017
requirements.txt add requirements Apr 20, 2017

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

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"

CartPole - Applying deep reinforcement learning on basic Cartpole game.

Atari - Mastering Atari games with Deep Reinforcement Learning

OpenAI GYM - [WIP]

  • Mountain Car - DQN
You can’t perform that action at this time.