An algorithm of pretrained fuzzy actor–critic learning applying in fixed-time space differential game
| Autor: | Wang Xiao, Peng Shi, Yushan Zhao, Howard M. Schwartz |
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| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Computer science Mechanical Engineering Aerospace Engineering 02 engineering and technology Fuzzy control system Space (commercial competition) Fuzzy logic 020901 industrial engineering & automation Fixed time Differential game 0202 electrical engineering electronic engineering information engineering Reinforcement learning 020201 artificial intelligence & image processing Algorithm |
| Zdroj: | Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering. 235:2095-2112 |
| ISSN: | 2041-3025 0954-4100 |
| DOI: | 10.1177/0954410021992439 |
| Popis: | Solving space differential game in an unknown environment remains a challenging problem. This article proposes a pretrained fuzzy actor–critic learning algorithm for dealing with the space pursuit-evasion game in fixed time. It is supposed that the research objects are two agents including one pursuer and one evader in space. A virtual environment, which is defined as the known part of the real environment, is utilized for deriving optimal strategies of the pursuer and the evader, respectively. Through employing the fuzzy inference system, a pretrained process, which is based on the genetic algorithm, is designed to obtain the initial consequent set of the pursuer and the evader. Besides, an actor–critic framework is applied to finely learn the suitable consequent set of the pursuer and evader in the real environment. Numerical experimental results validate the effectiveness of the proposed algorithms on improving the ability of the agents to adapt to the real environment. |
| Databáze: | OpenAIRE |
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