An Evolutionary Computation Framework for Multi-Agent Q-Learning with Mean-Field Environmental Feedback

Multi-agent reinforcement learning in networked populations is governed by the interaction between individual adaptation, local encounters, and changing environmental conditions. To study this interaction, we formulate a coupled learning--environment model in which agents update stateless $Q$-values