Generating Diverse and Competitive Play-Styles for Strategy Games

• URL: http://arxiv.org/abs/2104.08641v1
• Date: Sat, 17 Apr 2021 20:33:24 GMT
• Title: Generating Diverse and Competitive Play-Styles for Strategy Games
• Authors: Diego Perez-Liebana, Cristina Guerrero-Romero, Alexander Dockhorn, Dominik Jeurissen, Linjie Xu
• Abstract summary: We propose Portfolio Monte Carlo Tree Search with Progressive Unpruning for playing a turn-based strategy game (Tribes) We show how it can be parameterized so a quality-diversity algorithm (MAP-Elites) is used to achieve different play-styles while keeping a competitive level of play. Our results show that this algorithm is capable of achieving these goals even for an extensive collection of game levels beyond those used for training.
• Score: 58.896302717975445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Designing agents that are able to achieve different play-styles while maintaining a competitive level of play is a difficult task, especially for games for which the research community has not found super-human performance yet, like strategy games. These require the AI to deal with large action spaces, long-term planning and partial observability, among other well-known factors that make decision-making a hard problem. On top of this, achieving distinct play-styles using a general algorithm without reducing playing strength is not trivial. In this paper, we propose Portfolio Monte Carlo Tree Search with Progressive Unpruning for playing a turn-based strategy game (Tribes) and show how it can be parameterized so a quality-diversity algorithm (MAP-Elites) is used to achieve different play-styles while keeping a competitive level of play. Our results show that this algorithm is capable of achieving these goals even for an extensive collection of game levels beyond those used for training.

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