LEARNING TO SCHEDULE COMMUNICATION IN MULTI-AGENT REINFORCEMENT LEARNING

Many real-world reinforcement learning tasks require multiple agents to make sequential decisions under the agents’ interaction, where well-coordinated actions
among the agents are crucial to achieve the target goal better at these tasks. One
way to accelerate the coordination effect is to enable multiple agents to communicate with each other in a distributed manner and behave as a group. In this paper,
we study a practical scenario when (i) the communication bandwidth is limited
and (ii) the agents share the communication medium so that only a restricted number of agents are able to simultaneously use the medium, as in the state-of-the-art
wireless networking standards. This calls for a certain form of communication
scheduling. In that regard, we propose a multi-agent deep reinforcement learning framework, called SchedNet, in which agents learn how to schedule themselves, how to encode the messages, and how to select actions based on received
messages. SchedNet is capable of deciding which agents should be entitled to
broadcasting their (encoded) messages, by learning the importance of each agent’s
partially observed information. We evaluate SchedNet against multiple baselines
under two different applications, namely, cooperative communication and navigation, and predator-prey. Our experiments show a non-negligible performance gap
between SchedNet and other mechanisms such as the ones without communication and with vanilla scheduling methods, e.g., round robin, ranging from 32% to
43%.