mdasifhasan/Rainbow

Rainbow: Combining Improvements in Deep Reinforcement Learning

Rainbow

Rainbow: Combining Improvements in Deep Reinforcement Learning [1].

Results and pretrained models can be found in the releases.

• DQN [2]
• Double DQN [3]
• Prioritised Experience Replay [4]
• Dueling Network Architecture [5]
• Multi-step Returns [6]
• Distributional RL [7]
• Noisy Nets [8]

Requirements

• atari-py
• OpenCV Python
• Plotly
• PyTorch

To install all dependencies with Anaconda run conda env create -f environment.yml and use source activate rainbow to activate the environment.

Acknowledgements

• @floringogianu for categorical-dqn
• @jvmancuso for Noisy layer
• @jaara for AI-blog
• @openai for Baselines
• @mtthss for implementation details

References

[1] Rainbow: Combining Improvements in Deep Reinforcement Learning
[2] Playing Atari with Deep Reinforcement Learning
[3] Deep Reinforcement Learning with Double Q-learning
[4] Prioritized Experience Replay
[5] Dueling Network Architectures for Deep Reinforcement Learning
[6] Reinforcement Learning: An Introduction
[7] A Distributional Perspective on Reinforcement Learning
[8] Noisy Networks for Exploration