RLlib: Abstractions for Distributed Reinforcement Learning

This work argues 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, through RLlib: a library that provides scalable software primitives for RL.