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- W2045192098 abstract "Differential games (DGs), considered as a typical model of game with continuous states and non-linear dynamics, play an important role in control and optimization. Finding optimal/approximate solutions for these game in the imperfect information setting is currently a challenge for mathematicians and computer scientists. This article presents a multi-agent learning approach to this problem. We hence propose a method called resolution-based policy search, which uses a limited non-uniform discretization of a perfect information game version to parameterize policies to learn. We then study the application of this method to an imperfect information zero-sum pursuit-evasion game (PEG). Experimental results demonstrate strong performance of our method and show that it gives better solutions than those given by traditional analytical methods." @default.
- W2045192098 created "2016-06-24" @default.
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- W2045192098 date "2006-01-01" @default.
- W2045192098 modified "2023-09-27" @default.
- W2045192098 title "Resolution-Based Policy Search for Imperfect Information Differential Games" @default.
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- W2045192098 doi "https://doi.org/10.1109/iat.2006.108" @default.
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