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Cooperative tasks in Multi-Agent Reinforcement Learning (MARL) require agents to collectively maximize a shared return. ACPO addresses this by introducing a novel agent-chained policy optimization approach, which effectively computes policy gradients under the Centralized Training with Decentralized Execution (CTDE) paradigm. This unlocks scalable and efficient MARL for complex tasks.
“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) p…”
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ACTIVE
CATEGORY
Research
EVIDENCE
Not yet assessed
ENTITY
ACPO, MARL, CTDE
DECISION
Automated · no editorial override
LAST OBSERVED
Jul 22, 2026