RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem

Eric Liang · Zhanghao Wu · Michael Luo · Sven Mika · Joseph Gonzalez · Ion Stoica


Keywords: [ Reinforcement Learning and Planning ]

[ Abstract ]
[ Slides [ OpenReview
Thu 9 Dec 4:30 p.m. PST — 6 p.m. PST

Abstract: Researchers and practitioners in the field of reinforcement learning (RL) frequently leverage parallel computation, which has led to a plethora of new algorithms and systems in the last few years. In this paper, we re-examine the challenges posed by distributed RL and try to view it through the lens of an old idea: distributed dataflow. We show that viewing RL as a dataflow problem leads to highly composable and performant implementations. We propose RLlib Flow, a hybrid actor-dataflow programming model for distributed RL, and validate its practicality by porting the full suite of algorithms in RLlib, a widely adopted distributed RL library. Concretely, RLlib Flow provides 2-9$\times$ code savings in real production code and enables the composition of multi-agent algorithms not possible by end users before. The open-source code is available as part of RLlib at

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