AgentMixer: Multi-Agent Correlated Policy Factorization

In multi-agent reinforcement learning, centralized training with decentralized execution (CTDE) methods typically assume that agents make decisions based on their local observations independently, which may not lead to a correlated joint policy with coordination. Coordination can be explicitly encouraged during training and individual policies can be trained to imitate the correlated joint policy. However, this may lead to an textitasymmetric learning failure due to the observation mismatch between the joint and individual policies. Inspired by the concept of correlated equilibrium, we introduce a textitstrategy modification called AgentMixer that allows agents to correlate their policies. AgentMixer combines individual partially observable policies into a joint fully observable policy non-linearly. To enable decentralized execution, we introduce textitIndividual-Global-Consistency to guarantee mode consistency during joint training of the centralized and decentralized policies and prove that AgentMixer converges to an ε-approximate Correlated Equilibrium. In the Multi-Agent MuJoCo, SMAC-v2, Matrix Game, and Predator-Prey benchmarks, AgentMixer outperforms or matches state-of-the-art methods.