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Learning to communicate and act using hierarchical reinforcement learning

In this paper, we address the issue of rational communication behavior among autonomous agents. The goal is for agents to learn a policy to optimize the communication needed for proper coordination, given the communication cost. We extend our previously reported cooperative hierarchical reinforcement learning (HRL) algorithm to include communication decisions and propose a new multiagent HRL algorithm, called COM-Cooperative HRL. In this algorithm, we define cooperative subtasks to be those subtasks in which coordination among agents significantly improves the performance of the overall task. Those levels of the hierarchy which include cooperative subtasks are called cooperation levels. Coordination skills among agents are learned faster by sharing information at the cooperation levels, rather than the level of primitive actions. We add a communication level to the hierarchical decomposition of the problem below each cooperation level. Before making a decision at a cooperative subtask, agents decide if it is worthwhile to perform a communication action. A communication action has a certain cost and provides each agent at a certain cooperation level with the actions selected by the other agents at the same level. We demonstrate the efficacy of the COM-Cooperative HRL algorithm as well as the relation between the communication cost and the learned communication policy using a multiagent taxi domain.