Multi-Agent Synchronization Tasks
Keywords: Coordination, Multi-Agent Reinforcement Learning, Graph Neural Networks, Coordination Graphs, Multi-Agent Synchronization Tasks, Predator-Prey
TL;DR: This research paper introduces and investigates Multi-Agent Synchronization Tasks (MSTs) within the context of multi-agent reinforcement learning, focusing on the intricacies of coordination and cooperation.
Abstract: In multi-agent reinforcement learning (MARL), coordination plays a crucial role in enhancing agents' performance beyond what they could achieve through cooperation alone. The interdependence of agents' actions, coupled with the need for communication, leads to a domain where effective coordination is crucial. In this paper, we introduce and define $\textit{Multi-Agent Synchronization Tasks}$ (MSTs), a subset of multi-agent tasks. We describe one MST, that we call $\textit{Synchronized Predator-Prey}$, offering a detailed description that will serve as the basis for evaluating a selection of recent state-of-the-art (SOTA) MARL algorithms explicitly designed to address coordination challenges through the use of communication strategies. Furthermore, we present empirical evidence that reveals the limitations of the algorithms assessed to solve MSTs, demonstrating their inability to scale effectively beyond 2-agent coordination tasks in scenarios where communication is a requisite component. Finally, the results raise questions about the applicability of recent SOTA approaches for complex coordination tasks (i.e. MSTs) and prompt further exploration into the underlying causes of their limitations in this context.
Type Of Paper: Work-in-progress paper (max page 6)
Anonymous Submission: Anonymized submission.
Submission Number: 14
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