RLlib: Abstractions for Distributed Reinforcement Learning
  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.
