When an individual's behavior has rational characteristics, this may lead to
irrational collective actions for the group. A wide range of organisms from
animals to humans often evolve the social attribute of cooperation to meet this
challenge. Therefore, cooperation among individuals is of great significance
for allowing social organisms to adapt to changes in the natural environment.
Based on multi-agent reinforcement learning, we propose a new learning strategy
for achieving coordination by incorporating a learning rate that can balance
exploration and exploitation. We demonstrate that agents that use the simple
strategy improve a relatively collective return in a decision task called the
intertemporal social dilemma, where the conflict between the individual and the
group is particularly sharp. We also explore the effects of the diversity of
learning rates on the population of reinforcement learning agents and show that
agents trained in heterogeneous populations develop particularly coordinated
policies relative to those trained in homogeneous populations.

通过多智能体强化学习，将学习速率纳入协作策略以平衡探索和开发性，实现集体行为的协调。在决策任务中，简单的策略有助于提高相对集体的回报，并且异构环境下的强化学习代理人较同质环境更具协调性。