Go with the flow: reinforcement learning in turn-based battle video games
Loading...
Files
Accepted version
Date
2020-10
Authors
Pagalyte, Elinga
Mancini, Maurizio
Climent, Laura
Journal Title
Journal ISSN
Volume Title
Publisher
Association for Computing Machinery, ACM
Published Version
Abstract
Game flow represents a state where the player is neither frustrated nor bored. In turn-based battle video games it can be achieved by Dynamic Difficulty Adjustment (DDA), whose research has begun rising over the last decade. This paper introduces an idea for incorporating DDA through the use of Reinforcement Learning (RL) to agents of turn-based battle video games. We design and implement an RL agent that shows, in a simple environment, the idea of how a game could achieve balance through adequate choices in actions depending on the player's level of skill. For achieving this purpose, we incorporated the design and implementation of state-action-reward-state-action (SARSA) algorithm to the agent of our implemented game. In addition, we added tracking of the on-going games and depending on the frequency of the player's repeated wins or losses, the rewards of the RL agent are modified. This modification of the rewards has an impact on the RL agent's actions, which involves an increase/decrease of the difficulty of the battle game. The evaluation performed shows that the idea of the paper is demonstrated, since players face personalized challenges that we believe are in range of game flow.
Description
Keywords
DDA , Dynamic Difficulty Adjustment , Game Flow , Reinforcement Learning , RL , SARSA , Turn-based battle video game
Citation
Pagalyte, E., Mancini, M. and Climent, L. (2020) 'Go with the Flow: Reinforcement Learning in Turn-based Battle Video Games', Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents, IVA 2020 Virtual Event Scotland UK, 19-23 October. doi: 10.1145/3383652.3423868
Link to publisher’s version
Copyright
© 2020 Association for Computing Machinery. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).