Ray RLlib: Scalable Reinforcement Learning

https://github.com/ray-project/ray

A high-performance distributed execution engine

Ray is a flexible, high-performance distributed execution framework.

Ray comes with libraries that accelerate deep learning and reinforcement learning development:

  • Ray Tune: Hyperparameter Optimization Framework
  • Ray RLlib: Scalable Reinforcement Learning

More Information

  • Documentation
  • Tutorial
  • Blog
  • Ray paper
  • Ray HotOS paper

Ray RLlib: Scalable Reinforcement Learning

Ray RLlib is an RL execution toolkit built on the Ray distributed execution framework. See the user documentation and paper.

RLlib includes the following reference algorithms:

  • Proximal Policy Optimization (PPO) which is a proximal variant of TRPO.
  • Policy Gradients (PG).
  • Asynchronous Advantage Actor-Critic (A3C).
  • Deep Q Networks (DQN).
  • Deep Deterministic Policy Gradients (DDPG, DDPG2).
  • Ape-X Distributed Prioritized Experience Replay, including both DQN and DDPG variants.
  • Evolution Strategies (ES), as described in this paper.

These algorithms can be run on any OpenAI Gym MDP, including custom ones written and registered by the user.

原文发布于微信公众号 - CreateAMind(createamind)

原文发表时间:2018-05-25

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