arXiv paper proposes evolutionary framework for multi-agent Q-learning with mean-field feedback

A new arXiv preprint introduces an evolutionary computation approach to multi-agent reinforcement learning in networked populations. The framework combines individual adaptation, local interactions, and shifting environmental conditions through mean-field environmental feedback. The authors frame the work as a way to study how these coupled learning and environment dynamics interact.