A spatiotemporal cell theory for cooperative pattern formation in reinforcement learning-driven evolutionary games

Area of Science:

• Evolutionary Game Theory
• Artificial Intelligence
• Multi-Agent Systems
• Behavioral Economics

Background:

• Cooperation among self-interested agents is a fundamental challenge in biology, social dynamics, and AI.
• Traditional imitation-based models fail to explain complex cooperative patterns due to limited exploration and experience accumulation.

Purpose of the Study:

• To investigate cooperation mechanisms in the spatial snowdrift game using a multi-agent reinforcement learning framework.
• To elucidate the role of autonomous learning and experiential accumulation in driving cooperative evolution.
• To develop a spatiotemporal cell theory for analyzing the link between individual learning and collective behavior.

Main Methods:

• Developed a multi-agent reinforcement learning (MARL) framework for the spatial snowdrift game.
• Employed Q-learning for agents to accumulate interaction experience.
• Introduced a spatiotemporal cell theory to analyze microscopic learning and macroscopic pattern evolution.

Main Results:

• Agents using Q-learning achieved significantly higher cooperation levels than classical replicator dynamics.
• The system self-organized into robust collective decision-making structures.
• Identified an endogenous mechanism for cooperation driven solely by individual exploration and local feedback.

Conclusions:

• Efficient cooperation can emerge endogenously through autonomous learning and local interactions.
• The spatiotemporal cell theory provides a quantitative framework for understanding cooperative evolution.
• Derived a phase diagram for cooperative stability and identified distinct evolutionary pathways.