Step-by-step tutorials from DQN to Rainbow. Every tutorial contains both of theoretical backgrounds and object-oriented implementation. Just pick any topic in which you are interested, and learn and execute right away!

1. DQN [NBViewer] [Colab]
2. DoubleDQN [NBViewer] [Colab]
3. PrioritizedExperienceReplay [NBViewer] [Colab]
4. DuelingNet [NBViewer] [Colab]
5. NoisyNet [NBViewer] [Colab]
6. CategoricalDQN [NBViewer] [Colab]
7. N-stepLearning [NBViewer] [Colab]
8. Rainbow [NBViewer] [Colab]

This repository is tested on Anaconda virtual environment with python 3.6.1+

$ conda create -n rainbow_is_all_you_need python=3.6.1
$ conda activate rainbow_is_all_you_need

First, clone the repository.

git clone https://github.com/Curt-Park/rainbow-is-all-you-need.git
cd rainbow-is-all-you-need

Secondly, install packages required to execute the code. Just type:

make dep

1. V. Mnih et al., "Human-level control through deep reinforcement learning." Nature, 518 (7540):529–533, 2015.
2. van Hasselt et al., "Deep Reinforcement Learning with Double Q-learning." arXiv preprint arXiv:1509.06461, 2015.
3. T. Schaul et al., "Prioritized Experience Replay." arXiv preprint arXiv:1511.05952, 2015.
4. Z. Wang et al., "Dueling Network Architectures for Deep Reinforcement Learning." arXiv preprint arXiv:1511.06581, 2015.
5. M. Fortunato et al., "Noisy Networks for Exploration." arXiv preprint arXiv:1706.10295, 2017.
6. M. G. Bellemare et al., "A Distributional Perspective on Reinforcement Learning." arXiv preprint arXiv:1707.06887, 2017.
7. R. S. Sutton, "Learning to predict by the methods of temporal differences." Machine learning, 3(1):9–44, 1988.
8. M. Hessel et al., "Rainbow: Combining Improvements in Deep Reinforcement Learning." arXiv preprint arXiv:1710.02298, 2017.

Thanks goes to these wonderful people (emoji key):

Jinwoo Park (Curt) 💻 📖

Kyunghwan Kim 💻

This project follows the all-contributors specification. Contributions of any kind welcome!