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Reaching Consensus in Cooperative Multi-Agent Reinforcement Learning with Goal Imagination

arXiv 2403.03172 ↗First posted 5 Mar 2024cs.AI cs.LGcited by 0

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Abstract (arXiv)

Reaching consensus is key to multi-agent coordination. To accomplish a cooperative task, agents need to coherently select optimal joint actions to maximize the team reward. However, current cooperative multi-agent reinforcement learning (MARL) methods usually do not explicitly take consensus into consideration, which may cause miscoordination problem. In this paper, we propose a model-based consensus mechanism to explicitly coordinate multiple agents. The proposed Multi-agent Goal Imagination (MAGI) framework guides agents to reach consensus with an Imagined common goal. The common goal is an achievable state with high value, which is obtained by sampling from the distribution of future states. We directly model this distribution with a self-supervised generative model, thus alleviating the "curse of dimensinality" problem induced by multi-agent multi-step policy rollout commonly used in model-based methods. We show that such efficient consensus mechanism can guide all agents cooperatively reaching valuable future states. Results on Multi-agent Particle-Environments and Google Research Football environment demonstrate the superiority of MAGI in both sample efficiency and performance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 32 references that are in the map. First posted on arXiv 5 Mar 2024.

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References · 18

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6 more
  1. 2017Imagination-Augmented Agents for Deep Reinforcement Learningcited by 137
  2. 2020Towards Playing Full MOBA Games with Deep Reinforcement Learningcited by 78
  3. 2017Guided Deep Reinforcement Learning for Swarm Systemscited by 44
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  6. 2017Prediction and Control with Temporal Segment Modelscited by 14

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