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- W4245173692 abstract "A learning approach to stochastic parameter optimisation has been described in this paper. The possible parameter values are chosen by a pair of learning automata, which act as a team in a cooperative game. The automata are regarded as operating in an unknown random environment whose payoffs correspond to samples of the performance variable whose expectation is the performance index of the optimisation problem. Necessary and sufficient conditions on the functions in the reinforcement scheme are given for the expected payoff to be monotonically increasing in any arbitrary environment. Simulation results indicate that by a proper choice of the updating functions, the automata converge to optimum parameter values. The game approach appears to be one way of reducing the high dimensionality of decision space. As each participating automaton operates with no information regarding the other partner, the results of the paper are relevant to decentralised control." @default.
- W4245173692 created "2022-05-12" @default.
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- W4245173692 date "1982-01-01" @default.
- W4245173692 modified "2023-10-14" @default.
- W4245173692 title "ON LINE OPTIMIZATION WITH A TEAM OF LEARNING AUTOMATA" @default.
- W4245173692 doi "https://doi.org/10.1016/b978-0-08-027618-2.50051-6" @default.
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