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Researchers and practitioners in the field of reinforcement learning (RL)frequently leverage parallel computation, which has led to a plethora of newalgorithms and systems in the last few years. In this paper, we re-examine thechallenges posed by distributed RL and try to view it through the lens of anold idea: distributed dataflow. We show that viewing RL as a dataflow problemleads to highly composable and performant implementations. We propose RLlibFlow, a hybrid actor-dataflow programming model for distributed RL, andvalidate its practicality by porting the full suite of algorithms in RLlib, awidely adopted distributed RL library. Concretely, RLlib Flow provides 2-9 codesavings in real production code and enables the composition of multi-agentalgorithms not possible by end users before. The open-source code is availableas part of RLlib at https://github.com/ray-project/ray/tree/master/rllib.

RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem