Learning optimal behavior policy for each agent in multi-agent systems is an
essential yet difficult problem. Despite fruitful progress in multi-agent
reinforcement learning, the challenge of addressing the dynamics of whether two
agents should exhibit consistent behaviors is still under-explored. In this
paper, we propose a new approach that enables agents to learn whether their
behaviors should be consistent with that of other agents by utilizing intrinsic
rewards to learn the optimal policy for each agent. We begin by defining
behavior consistency as the divergence in output actions between two agents
when provided with the same observation. Subsequently, we introduce dynamic
consistency intrinsic reward (DCIR) to stimulate agents to be aware of others'
behaviors and determine whether to be consistent with them. Lastly, we devise a
dynamic scale network (DSN) that provides learnable scale factors for the agent
at every time step to dynamically ascertain whether to award consistent
behavior and the magnitude of rewards. We evaluate DCIR in multiple
environments including Multi-agent Particle, Google Research Football and
StarCraft II Micromanagement, demonstrating its efficacy.

为了解决多智能体系统中智能体学习最优行为策略的问题，本文提出了一种新方法来利用内部奖励使智能体能够学习是否应该与其他智能体保持一致的行为，并通过动态一致性内部奖励 (DCIR) 和动态比例网络 (DSN) 来评估其效果。