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Efficient Exploration for Multi-Agent Reinforcement Learning via Transferable successor features

IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1673-1686页

作者： Wenzhang Liu Lu Dong Dan Niu Changyin Sun the School of Artificial Intelligence Anhui UniversityHefei 230039and also with the Peng Cheng LaboratoryShenzhen 518055China the School of Cyber Science and Engineering Southeast UniversityNanjing 211189China the School of Automation Southeast UniversityNanjing 210096China the School of Automation Southeast UniversityNanjing 210096and also with the Peng Cheng LaboratoryShenzhen 518055China

In multi-agent reinforcement learning(MARL),the behaviors of each agent can influence the learning of others,and the agents have to search in an exponentially enlarged joint-action ***,it is challenging for the multi-agent teams to explore in the *** may achieve suboptimal policies and fail to solve some complex *** improve the exploring efficiency as well as the performance of MARL tasks,in this paper,we propose a new approach by transferring the knowledge across *** from the traditional MARL algorithms,we first assume that the reward functions can be computed by linear combinations of a shared feature function and a set of taskspecific ***,we define a set of basic MARL tasks in the source domain and pre-train them as the basic knowledge for further ***,once the weights for target tasks are available,it will be easier to get a well-performed policy to explore in the target ***,the learning process of agents for target tasks is speeded up by taking full use of the basic knowledge that was learned *** evaluate the proposed algorithm on two challenging MARL tasks:cooperative boxpushing and non-monotonic *** experiment results have demonstrated the improved performance compared with state-of-the-art MARL algorithms.

关键词： Knowledge transfer multi-agent systems reinforcement learning successor features

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