<ul><li>Agents in multi-agent reinforcement learning (MARL) struggle to assess the relevance of input information for cooperative tasks.</li><li>In communication-limited scenarios, agents are unable to access global information, limiting their collaboration abilities.</li><li>A novel cooperative MARL framework based on information selection and tacit learning is introduced.</li><li>The framework enables agents to develop implicit coordination and adaptively filter information for enhanced decision-making capabilities.</li></ul>

Tacit Learning with Adaptive Information Selection for Cooperative Multi-Agent Reinforcement Learning

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