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FACMAC factored multi-agent centralized policy gradients

Contribution grade

Cooperative multi-agent actor-critic · Human-designed research method or resource

Reference score105,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Learns a factored centralized critic and differentiates a joint current-policy action to coordinate actor updates; examines monotonic and nonmonotonic critic factorization in cooperative control.

Scientific scope

Learns a factored centralized critic and differentiates a joint current-policy action to coordinate actor updates; examines monotonic and nonmonotonic critic factorization in cooperative control. The controlling v5 is dated 7 May 2021. The same identifier's 2020 v1 describes COMIX/FacMADDPG with different methods and six-author order, and is not used to backdate FACMAC. Nonmonotonic factorization is allowed because actors perform decentralized inference; no global optimum is guaranteed. First three v5 authors are equal contributors.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. The controlling v5 is dated 7 May 2021. The same identifier's 2020 v1 describes COMIX/FacMADDPG with different methods and six-author order, and is not used to backdate FACMAC. Nonmonotonic factorization is allowed because actors perform decentralized inference; no global optimum is guaranteed. First three v5 authors are equal contributors.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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