Value Factorization for Asynchronous Multi-Agent Reinforcement Learning
Primary Area: reinforcement learning
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Keywords: Reinforcement Learning, Asynchronous Multi-Agent Reinforcement Learning, Macro-Actions
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TL;DR: Our work introduces value factorization in the asynchronous framework to generalize the synchronous case and solve challenging coordination tasks where synchronous methods fail.
Abstract: Value factorization has become widely used to design high-quality and scalable multi-agent reinforcement learning algorithms. However, existing methods assume agents execute synchronously, which does not align with the asynchronous nature of real-world multi-agent systems. In these systems, agents often make decisions at different times, executing asynchronous (*macro-*)actions characterized by varying and unknown duration. Our work introduces value factorization to the asynchronous framework. To this end, we formalize the consistency requirement between joint and individual macro-action selection, proving it generalizes the synchronous case. We then propose approaches that use asynchronous centralized information to enable factorization architectures to support macro-actions. We evaluate the resultant asynchronous value factorization algorithms across increasingly complex domains that are standard benchmarks in the macro-action literature. Crucially, the proposed methods scale well in these challenging coordination tasks where their synchronous counterparts fail.
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Submission Number: 5344
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