RLlib: Abstractions for Distributed Reinforcement Learning

Eric Liang; Richard Liaw; Philipp Moritz; Robert Nishihara; Roy Fox; Ken Goldberg; Joseph E. Gonzalez; Michael I. Jordan; Ion Stoica

arXiv · 1712.09381

RLlib: Abstractions for Distributed Reinforcement Learning

Eric Liang·Richard Liaw·Philipp Moritz·Robert Nishihara·Roy Fox·Ken Goldberg·Joseph E. Gonzalez·Michael I. Jordan·Ion Stoica

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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/.

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BibTeX RIS

Eric Liang, Richard Liaw, Philipp Moritz, Robert Nishihara, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica. 2017-12-26. RLlib: Abstractions for Distributed Reinforcement Learning. https://arxiv.org/abs/1712.09381

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