  Cooperative behavior is prevalent in both human society and nature.
Understanding the emergence and maintenance of cooperation among
self-interested individuals remains a significant challenge in evolutionary
biology and social sciences. Reinforcement learning (RL) provides a suitable
framework for studying evolutionary game theory as it can adapt to
environmental changes and maximize expected benefits. In this study, we employ
the State-Action-Reward-State-Action (SARSA) algorithm as the decision-making
mechanism for individuals in evolutionary game theory. Initially, we apply
SARSA to imitation learning, where agents select neighbors to imitate based on
rewards. This approach allows us to observe behavioral changes in agents
without independent decision-making abilities. Subsequently, SARSA is utilized
for primary agents to independently choose cooperation or betrayal with their
neighbors. We evaluate the impact of SARSA on cooperation rates by analyzing
variations in rewards and the distribution of cooperators and defectors within
the network.
