  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.
