A brief review of evolutionary game dynamics in the reinforcement learning paradigm

TL;DR

This review explores how reinforcement learning enhances evolutionary game dynamics models, providing insights into cooperation, trust, fairness, and ecological interactions beyond traditional imitation-based approaches.

Contribution

It synthesizes recent advances applying reinforcement learning to evolutionary game theory, highlighting its potential to unify understanding of social and ecological phenomena.

Findings

RL models better explain cooperation and fairness phenomena

Reinforcement learning offers a unified framework for social dynamics

Studies show RL improves ecological and resource coordination understanding

Abstract

Cooperation, fairness, trust, and resource coordination are cornerstones of modern civilization, yet their emergence remains inadequately explained by the persistent discrepancies between theoretical predictions and behavioral experiments. Part of this gap may arise from the imitation learning paradigm commonly used in prior theoretical models, which assumes individuals merely copy successful neighbors according to predetermined, fixed rules. This review examines recent advances in evolutionary game dynamics that employ reinforcement learning (RL) as an alternative paradigm. In RL, individuals learn through trial and error and introspectively refine their strategies based on environmental feedback. We begin by introducing key concepts in evolutionary game theory and the two learning paradigms, then synthesize progress in applying RL to elucidate cooperation, trust, fairness, optimal resource coordination, and ecological dynamics. Collectively, these studies indicate that RL offers a promising unified framework for understanding the diverse social and ecological phenomena observed in human and natural systems.