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MATES: Learning Multi-Agent Interactions by Transforming Observations for Frozen Single-Agent Policies
Multi-agent reinforcement learning (MARL) commonly trains decentralized policies from scratch, requiring agents to acquire individual task competence and coordination simultaneously. Yet many multi-agent problems admit a compatible single-agent counterpart in which the underlying task can be learned
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