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Proceedings Paper

Mean field game and decentralized intelligent adaptive pursuit evasion strategy for massive multi-agent system under uncertain environment (Conference Presentation)
Author(s): Zejian Zhou; Hao Xu

Paper Abstract

In this paper, a novel decentralized intelligent adaptive optimal strategy has been developed to solve the pursuit-evasion game for massive Multi-Agent Systems (MAS) under uncertain environment. Existing strategies for pursuit-evasion games are neither efficient nor practical for large population multi-agent system due to the notorious ``Curse of dimensionality" and communication limit while the agent population is large. To overcome these challenges, the emerging mean field game theory is adopted and further integrated with reinforcement learning to develop a novel decentralized intelligent adaptive strategy with a new type of adaptive dynamic programing architecture named the Actor-Critic-Mass (ACM). Through online approximating the solution of the coupled mean field equations, the developed strategy can obtain the optimal pursuit-evasion policy even for massive MAS under uncertain environment.

Paper Details

Date Published: 27 April 2020
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Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 114131F (27 April 2020); doi: 10.1117/12.2556222
Show Author Affiliations
Zejian Zhou, Univ. of Nevada, Reno (United States)
Hao Xu, Univ. of Nevada, Reno (United States)


Published in SPIE Proceedings Vol. 11413:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II
Tien Pham; Latasha Solomon; Katie Rainey, Editor(s)

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