Energy-based Surprise Minimization for Multi-Agent Value Factorization
This addresses challenges in multi-agent value factorization for partially-observable settings, though it appears incremental as it builds on existing methods like Maxmin Q-learning.
The paper tackles the problems of surprise across spurious states and approximation bias in multi-agent reinforcement learning by introducing EMIX, an algorithm that minimizes surprise using energy across agents, demonstrating consistent stable performance in StarCraft II scenarios.
Multi-Agent Reinforcement Learning (MARL) has demonstrated significant success in training decentralised policies in a centralised manner by making use of value factorization methods. However, addressing surprise across spurious states and approximation bias remain open problems for multi-agent settings. Towards this goal, we introduce the Energy-based MIXer (EMIX), an algorithm which minimizes surprise utilizing the energy across agents. Our contributions are threefold; (1) EMIX introduces a novel surprise minimization technique across multiple agents in the case of multi-agent partially-observable settings. (2) EMIX highlights a practical use of energy functions in MARL with theoretical guarantees and experiment validations of the energy operator. Lastly, (3) EMIX extends Maxmin Q-learning for addressing overestimation bias across agents in MARL. In a study of challenging StarCraft II micromanagement scenarios, EMIX demonstrates consistent stable performance for multiagent surprise minimization. Moreover, our ablation study highlights the necessity of the energy-based scheme and the need for elimination of overestimation bias in MARL. Our implementation of EMIX can be found at karush17.github.io/emix-web/.