A framework for designing reinforcement learning agents with dynamic difficulty adjustment in single-player action video games
Dynamic Difficulty Adjustment (DDA) within video games aims to avoid frustration or boredom. This paper provides the first framework for designing a Reinforcement Learning (RL) agent with DDA for single-player action video games. The framework includes the definitions of states, actions, and rewards...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/714295 |
| Acceso en línea: | http://hdl.handle.net/10486/714295 https://dx.doi.org/10.1016/j.entcom.2024.100686 |
| Access Level: | acceso embargado |
| Palabra clave: | Dynamic Difficulty Adjustment and Single-Player Action Video Games Reinforcement Learning Agent Informática |
| Sumario: | Dynamic Difficulty Adjustment (DDA) within video games aims to avoid frustration or boredom. This paper provides the first framework for designing a Reinforcement Learning (RL) agent with DDA for single-player action video games. The framework includes the definitions of states, actions, and rewards. We propose a 2Q-table system that can provide a better winning/losing ratio and extend the duration of the rounds. We apply the framework to a use case study. We address the challenges that the design and implementation of RL agents with DDA for single-player action video games might present, such as (i) large and/or continuous action–state spaces, (ii) an appropriate definition of the rewards for achieving a correct DDA, (iii) learning from each player online from limited samples and (iv) in an arcade shooter video game. The two evaluations performed (with computer-driven and human players) show that the paper's goals are met since players face personalized challenges according to their playing skills |
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