Title: RLlib: Abstractions for Distributed Reinforcement Learning,
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
this https URL.