Hierarchical Multiagent Reinforcement Learning
MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER SCIENCE
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In this paper, we investigate the use of hierarchical reinforcement learning HRL to speed up the acquisition of cooperative multiagent tasks. We introduce a hierarchical multiagent reinforcement learning RL framework and propose a hierarchical multiagent RL algorithm called Cooperative HRL. In our approach, agents are cooperative and homogeneous use the same task decomposition. Learning is decentralized, with each agent learning three interrelated skills how to perform subtasks, which order to do them in, and how to coordinate with other agents. 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. Since coordination at high levels allows for increased cooperation skills as agents do not get confused by low-level details, we usually define cooperative subtasks at the high levels of the hierarchy.