JAX implementation of Reinforcement Learning agents
Project description
Jaxagents
Jaxagents is a Python implementation of Reinforcement Learning agents built upon JAX.
Content
So far, the project includes the following agents:
- Q-learning:
- Deep Q Networks (DQN)
- Double Deep Q Networks (DDQN)
- Categorical Deep Q Networks (often known as C51)
- Quantile Regression Deep Q Networks (QRDQN)
- Policy gradient:
- REINFORCE
- PPO with clipping and GAE
Background
Research and development in Reinforcement Learning can be computationally cumbersome. Utilizing JAX's high computational performance, Jaxagents provides a framework for applying and developing Reinforcement Learning agents that offers benefits in:
- computational speed
- easy control of random number generation
- hyperparameter optimization (via parallelized calculations)
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