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The Search of the Optimal Strategy in Economic Simulation Game for Players and Developers on the Example of Travian

ФИО студента: Tabakaev Nikita

Руководитель: Mariam Mamedli

Кампус/факультет: Faculty of Economic Sciences

Программа: Economics (Bachelor)

Год защиты: 2021

This study examines the way to build up a model and find optimal strategies for players and design developers in economic simulation games on the example of Travian through deep reinforcement learning algorithms. Imagine yourself as a creator of some strategy game or sport event where participantsare able to waste fixed amount of money to boost their performance at the beginning, and your goal is to maximize profit which is calculated as expected players’ initial expenditures. There are following examples: videogamers can buy additional primordial resources and sportsmen can improve their training conditions such as better equipment and coaching staff rentedfrom championships’ sponsors. First of all we are to find optimal players’ behavior with respect to different basic parameters installed by developers (game conditions equal to any player). Deep reinforcement learning methods will be implemented for this step because most of difficult games with communications seem to be analytically unsolvable. Finally, we expect strong interpretive validation and evaluation of marginal effects (how surely participants increase their chances with an additional money unit spent for competitions with varied designs) to solve developers’ profit maximization problem

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