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TANDEM: Balancing Centralized and Decentralized Critics to Address the Information Gap in Complex Multi-Agent Environments
Cooperative multi-agent reinforcement learning under centralized training with decentralized execution (CTDE) shapes decentralized policies with a centralized critic that observes the global state, then removes the critic at execution. The policies are optimized to rely on information they will not
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