RLlib Flow: Distributed Reinforcement Learning is a Dataflow ProblemDownload PDF

21 May 2021, 20:45 (modified: 31 Jan 2022, 04:56)NeurIPS 2021 PosterReaders: Everyone
Keywords: Distributed Reinforcement Learning, Dataflow Programming Model
TL;DR: We propose a hybrid actor-dataflow programming model, RLlib Flow, for distributed RL that significantly reduce the implementation complexity in real production code.
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 https://github.com/ray-project/ray/tree/master/rllib.
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Code: https://github.com/ray-project/ray/tree/master/rllib
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