Cooperative Game-Theoretic Credit Assignment for Multi-Agent Policy Gradients via the Core

ArXi:2506.04265v3 Announce Type: replace-cross This work focuses on the credit assignment problem in cooperative multi-agent reinforcement learning (MARL). Sharing the global advantage among agents often leads to insufficient policy optimization, as it fails to capture the coalitional contributions of different agents. In this work, we revisit the policy update process from a coalitional perspective and propose CORA, an advantage allocation method guided by a cooperative game-theoretic core allocation.