학술논문

Analysis of Equilibrium Strategies in a New Number-Guessing Game with Reward and Penalty / 報酬とペナルティを導入した数当てゲームの提案と均衡戦略の分析
Document Type
Journal Article
Source
Proceedings of the Annual Conference of JSAI. 2023, :4
Subject
Game theory
Min-Max Q learning
Min-Max Q学習
Multi-agent reinforcement learning
ゲーム理論
マルチエージェント強化学習
Language
Japanese
ISSN
2758-7347
Abstract
We propose a new variant of number-guessing games with penalties for failure and consider the equilibrium strategies in the game. In the proposed game, the codemaker selects a number from 1 to n as her private information then the codebreaker guesses the number. The codebreaker can receive the number as her reward when she guesses correctly, but she must pay a penalty for each failed guess. We formalize the game as a linear programming problem to obtain the codemaker's Min-Max strategy and the codebreaker's Max-Min strategy. The strategies are also explored by using Minimax Q Learning. We compare the computational cost of the two approaches in obtaining the equilibrium strategies.

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