文章基本信息

标题：Multi-Agent Reinforcement Learning with Optimal Equivalent Action of Neighborhood
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作者：Haixing Wang ; Yi Yang ; Zhiwei Lin 等
期刊名称：Actuators
电子版ISSN：2076-0825
出版年度：2022
卷号：11
期号：4
页码：99
DOI：10.3390/act11040099
语种：English
出版社：MDPI Publishing
摘要：In a multi-agent system, the complex interaction among agents is one of the difficulties in making the optimal decision. This paper proposes a new action value function and a learning mechanism based on the optimal equivalent action of the neighborhood (OEAN) of a multi-agent system, in order to obtain the optimal decision from the agents. In the new Q-value function, the OEAN is used to depict the equivalent interaction between the current agent and the others. To deal with the non-stationary environment when agents act, the OEAN of the current agent is inferred simultaneously by the maximum a posteriori based on the hidden Markov random field model. The convergence property of the proposed methodology proved that the Q-value function can approach the global Nash equilibrium value using the iteration mechanism. The effectiveness of the method is verified by the case study of the top-coal caving. The experiment results show that the OEAN can reduce the complexity of the agents’ interaction description, meanwhile, the top-coal caving performance can be improved significantly.