Abstract
The problems that occurred during decision-making and supported by the stochastic game model with the environment are investigated and analysed. A method of adaptive choice of solutions based on stimulatory learning and application of Boltzmann distribution is developed. The algorithm and software of the decision-making system in the games with the environment are developed. The computer simulation results of stochastic choice of solutions are analysed and described. The developed decision-making method provides stochastic minimisation of the agent’s mean loss function and is based on adaptive parametric identification of the environment using Q-learning and defining a mixed strategy on the Boltzmann distribution. With proper parameter selection, the method provides close to 1 order of asymptotic convergence, the limit value for a given class of recurrent methods.
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Kravets, P., Vysotska, V., Lytvyn, V., Chyrun, L. (2023). Adaptive Decision-Making Strategies in the Game with Environment. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. ISDMCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-031-16203-9_17
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