  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/.
