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keras-rl implements some state-of-the-art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. Furthermore, keras-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy. Of course, you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. Documentation is available online.
Features
- Deep Q Learning (DQN)
- Deep Deterministic Policy Gradient (DDPG)
- Asynchronous Advantage Actor-Critic (A3C)
- Documentation available
- Proximal Policy Optimization Algorithms (PPO)
- Cross-Entropy Method (CEM)
- Examples available
Project Samples
Categories
Machine Learning, Reinforcement Learning Frameworks, Reinforcement Learning Libraries, Reinforcement Learning AlgorithmsLicense
MIT LicenseFollow Deep Reinforcement Learning for Keras
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