Research on Multi-Agent Reinforcement Learning in Snowdrift Game

With the increasing application of artificial intelligence in decision science, it becomes greatly important to understand and simulate the strategy choices of individuals in complex interactive environments. As a model to simulate the dynamics of cooperation and betrayal, the snowdrift game provides researchers with a useful tool to analyze individual behavior strategies. This study aims to explore the application of reinforcement learning algorithms in snowdrift games. In this study, a computational model to simulate a snowdrift game is constructed by introducing a Q-learning algorithm. The influence of key parameters such as betrayal factor, cooperation factor, and exploration rate on strategy selection is also investigated. This paper experimentally analyzes how these factors affect the algorithm's decision-making process, which can promote the emergence of cooperative behavior and make strategic choices beneficial to collective benefits in an uncertain environment.