Containerized Distributed Value-Based Multi-Agent Reinforcement LearningDownload PDF


Sep 29, 2021 (edited Oct 06, 2021)ICLR 2022 Conference Blind SubmissionReaders: Everyone
  • Keywords: Multi-agent reinforcement learning, Distributed reinforcement learning
  • Abstract: Multi-agent reinforcement learning tasks put a high demand on the volume of training samples. Different from its single-agent counterpart, distributed value-based multi-agent reinforcement learning faces the unique challenges of demanding data transfer, inter-process communication management, and high requirement of exploration. We propose a containerized learning framework to solve these problems. We pack several environment instances, a local learner and buffer, and a carefully designed multi-queue manager which avoids blocking into a container. Local policies of each container are encouraged to be as diverse as possible, and only trajectories with highest priority are sent to a global learner. In this way, we achieve a scalable, time-efficient, and diverse distributed MARL learning framework with high system throughput. To own knowledge, our method is the first to solve the challenging Google Research Football full game $\mathtt{5\_v\_5}$. On the StarCraft II micromanagement benchmark, our method gets 4-18$\times$ better results compared to state-of-the-art non-distributed MARL algorithms.
  • Supplementary Material: zip
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