An implementation of Rainbow in PyTorch. A lot of codes are borrowed from baselines, NoisyNet-A3C, RL-Adventure.

List of papers are:

1. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529. https://doi.org/10.1038/nature14236

2. van Hasselt, H., Guez, A., & Silver, D. (2015, September 22). Deep Reinforcement Learning with Double Q-learning. arXiv.org.

3. Schaul, T., Quan, J., Antonoglou, I., & Silver, D. (2015, November 19). Prioritized Experience Replay. arXiv.org.

4. Wang, Z., Schaul, T., Hessel, M., van Hasselt, H., Lanctot, M., & de Freitas, N. (2015, November 20). Dueling Network Architectures for Deep Reinforcement Learning. arXiv.org.

5. Fortunato, M., Azar, M. G., Piot, B., Menick, J., Osband, I., Graves, A., et al. (2017, July 1). Noisy Networks for Exploration. arXiv.org.

6. Hessel, M., Modayil, J., van Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., et al. (2017, October 6). Rainbow: Combining Improvements in Deep Reinforcement Learning. arXiv.org.

torch
torchvision
numpy
tensorboardX
opencv-python

Training:

python main.py \
--max-frames 12000 \
--env Breakout-ram-v4 \
--save-model "test" 

Evaluation:

python main.py \
--evaluate \
--env Breakout-ram-v4 \
--load-model "dqn-test"

• Kalxhin
• higgsfield
• OpenAI
• Jungsub Lim