RLlib: Abstractions for Distributed Reinforcement Learning

ICML 2018 Eric LiangRichard LiawPhilipp MoritzRobert NishiharaRoy FoxKen GoldbergJoseph E. GonzalezMichael I. JordanIon Stoica

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... (read more)

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