RLlib: Abstractions for Distributed Reinforcement Learning

Code

• github.com/ray-project/ray
  OfficialIn papertf★ 41,851
• github.com/susumuota/distributed_experience_replay
  tf★ 0
• github.com/AmeerHajAli/ray2
  tf★ 0

Abstract

Reinforcement learning (RL) algorithms involve the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation. We argue for distributing RL components in a composable way by adapting algorithms for top-down hierarchical control, thereby encapsulating parallelism and resource requirements within short-running compute tasks. We demonstrate the benefits of this principle through RLlib: a library that provides scalable software primitives for RL. These primitives enable a broad range of algorithms to be implemented with high performance, scalability, and substantial code reuse. RLlib is available at https://rllib.io/.

Tasks

Reproductions