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27 April 2020 Mean field game and decentralized intelligent adaptive pursuit evasion strategy for massive multi-agent system under uncertain environment (Conference Presentation)
Zejian Zhou, Hao Xu
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Proceedings Volume 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II; 114131F (2020) https://doi.org/10.1117/12.2556222
Event: SPIE Defense + Commercial Sensing, 2020, Online Only
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.
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Zejian Zhou and Hao Xu "Mean field game and decentralized intelligent adaptive pursuit evasion strategy for massive multi-agent system under uncertain environment (Conference Presentation)", Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 114131F (27 April 2020); https://doi.org/10.1117/12.2556222
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KEYWORDS
Strategic intelligence
Neural networks
Telecommunications
Numerical simulations
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