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Multi-agent reinforcement learning shines as the pinnacle of multi-agentsystems, conquering intricate real-world challenges, fostering collaborationand coordination among agents, and unleashing the potential for intelligentdecision-making across domains. However, training a multi-agent reinforcementlearning network is a formidable endeavor, demanding substantial computationalresources to interact with diverse environmental variables, extract staterepresentations, and acquire decision-making knowledge. The recentbreakthroughs in large-scale pre-trained models ignite our curiosity: Can weuncover shared knowledge in multi-agent reinforcement learning and leveragepre-trained models to expedite training for future tasks? Addressing thisissue, we present an innovative multi-task learning approach that aims toextract and harness common decision-making knowledge, like cooperation andcompetition, across different tasks. Our approach involves concurrent trainingof multiple multi-agent tasks, with each task employing independent front-endperception layers while sharing back-end decision-making layers. This effectivedecoupling of state representation extraction from decision-making allows formore efficient training and better transferability. To evaluate the efficacy ofour proposed approach, we conduct comprehensive experiments in two distinctenvironments: the StarCraft Multi-agent Challenge (SMAC) and the GoogleResearch Football (GRF) environments. The experimental results unequivocallydemonstrate the smooth transferability of the shared decision-making network toother tasks, thereby significantly reducing training costs and improving finalperformance. Furthermore, visualizations authenticate the presence of generalmulti-agent decision-making knowledge within the shared network layers, furthervalidating the effectiveness of our approach.

Multi-Task Multi-Agent Shared Layers are Universal Cognition of Multi-Agent Coordination