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Combining improvements in deep reinforcement learning. Results and pretrained models can be found in the releases. Data-efficient Rainbow can be run using several options (note that the "unbounded" memory is implemented here in practice by manually setting the memory capacity to be the same as the maximum number of timesteps).
Features
- Prioritised Experience Replay
- Dueling Network Architecture
- Multi-step Returns
- Run the original Rainbow with the default arguments
- Requires atari-py
- Requires OpenCV Python
Project Samples
Categories
Machine Learning, Reinforcement Learning Frameworks, Reinforcement Learning Libraries, Reinforcement Learning AlgorithmsLicense
MIT LicenseFollow Rainbow
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