ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning

Understand

Value function factorization methods have become a dominant approach for cooperative multiagent reinforcement learning under a centralized training and decentralized execution paradigm.

• By factorizing the optimal joint action-value function using a monotonic mixing function of agents' utilities, these algorithms ensure the consistency between joint and local action selections for decentralized decision-making.
• Nevertheless, the use of monotonic mixing functions also induces representational limitations.
• Finding the optimal projection of an unrestricted mixing function onto monotonic function classes is still an open problem.