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News/ACPO: Agent-Chained Policy Optimization for Multi-Agent Reinforcement Learning
arxiv
PublishedJuly 20, 2026 at 4:00 AM
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ACPO: Agent-Chained Policy Optimization for Multi-Agent Reinforcement Learning

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arXiv:2606.30072v2 Announce Type: replace Abstract: Cooperative tasks in Multi-Agent Reinforcement Learning (MARL) require agents to collectively maximize a shared return. Under the Centralized Training with Decentralized Execution (CTDE) paradigm, policy gradients have remained difficult to compute

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