Reinforcement Learning in Digital Games: An Exploration of AI in Gaming
- DOI
- 10.2991/978-94-6463-300-9_37How to use a DOI?
- Keywords
- Reinforcement learning; digital games; AI
- Abstract
Reinforcement Learning (RL), a pivotal technique in AI, finds extensive applications in game AI, ranging from elementary board games to sophisticated strategy games. This application not only carries significant practical implications but also catalyzes theoretical advancements in AI. The objective of this paper is to conduct a comprehensive review and analysis of the current application of RL in game AI, with the intent of uncovering its potential for augmenting the gaming experience. This research focuses on exploring five primary RL methodologies and their implementation in specific games. The aim is to gain a deeper understanding of the advantages, limitations, and impact of these methods on the gaming experience. The insights gleaned from this research endeavor will foster a more nuanced understanding of the practical implementation of RL, serving as an invaluable resource for subsequent research and development pursuits. Additionally, this paper analyzes and discusses the challenges and characteristics that AI games may face in the future. Finally, the paper concludes with a summary and prospects for future research.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Ziwei Tang PY - 2023 DA - 2023/11/27 TI - Reinforcement Learning in Digital Games: An Exploration of AI in Gaming BT - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023) PB - Atlantis Press SP - 361 EP - 370 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-300-9_37 DO - 10.2991/978-94-6463-300-9_37 ID - Tang2023 ER -