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

arXiv · AI, language, vision and robotics · article · Sep 5, 2026 · UTC

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 on a fixed graph, while their population-average behavior drives an environmental variable that dynamically modifies the payoff matrix. Under a first-order mean-field closure, we derive a deterministic transport equation for the population distribution of $Q$-values and couple it with a projected d

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