Improve Counterfactual Regret Minimization Agents Training by Setting Limitations ofNumbers of Steps in Games

• Item type: Symposium(1)
• 公開日: 2021-11-06
• タイトル: Improve Counterfactual Regret Minimization Agents Training by Setting Limitations ofNumbers of Steps in Games
• タイトル / 言語: en
• タイトル: Improve Counterfactual Regret Minimization Agents Training by Setting Limitations ofNumbers of Steps in Games
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: Imperfect Information Games
• キーワード / 主題Scheme: Other
• キーワード / 主題: Counterfactual Regret Minimization
• キーワード / 主題Scheme: Other
• キーワード / 主題: Abstraction technique
• キーワード / 主題Scheme: Other
• キーワード / 主題: Curriculum Learning
• キーワード / 主題Scheme: Other
• キーワード / 主題: Card Game Cheat
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_5794
• 資源タイプ: conference paper
• 著者所属: Graduate School of Arts and Sciences, the University of Tokyo
• 著者所属: Graduate School of Arts and Sciences, the University of Tokyo
• 著者所属(英): en
• 著者所属(英): Graduate School of Arts and Sciences, the University of Tokyo
• 著者所属(英): en
• 著者所属(英): Graduate School of Arts and Sciences, the University of Tokyo
• 著者名: Cheng, Yi; Tomoyuki, Kaneko
• 著者名(英): Cheng, Yi; Tomoyuki, Kaneko
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Counterfactual Regret Minimization (CFR) has been one of the most famous algorithms to learn decent strategies of imperfect information games. Because CFR requires traversing the whole or part of game tree every iteration, it is infeasible to handle games with repetition where the game tree is not finite. In this paper, we introduce two abstraction techniques, one of which is to make the game tree finite and the other one is to reduce the size of game trees. Our experiments are conducted in an imperfect information card game called Cheat and we introduce the notion of “Health Points” a player has in each game to make the game length finite thus easier to handle. We utilize the information sets abstraction technique to speedup the training and evaluate how results from smaller games can improve training in larger ones. We also show Ordered Abstraction can help us increase the learning efficiency of specific agents.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Counterfactual Regret Minimization (CFR) has been one of the most famous algorithms to learn decent strategies of imperfect information games. Because CFR requires traversing the whole or part of game tree every iteration, it is infeasible to handle games with repetition where the game tree is not finite. In this paper, we introduce two abstraction techniques, one of which is to make the game tree finite and the other one is to reduce the size of game trees. Our experiments are conducted in an imperfect information card game called Cheat and we introduce the notion of “Health Points” a player has in each game to make the game length finite thus easier to handle. We utilize the information sets abstraction technique to speedup the training and evaluate how results from smaller games can improve training in larger ones. We also show Ordered Abstraction can help us increase the learning efficiency of specific agents.
• 書誌情報: ゲームプログラミングワークショップ2021論文集; 巻 2021, p. 117-123, 発行日 2021-11-06
• 出版者 / 言語: ja
• 出版者: 情報処理学会