Reinforcement learning in first person shooter games

McPartland, Michelle and Gallagher, Marcus (2011) Reinforcement learning in first person shooter games. Ieee Transactions On Computational Intelligence and Ai in Games, 3 1: 43-56. doi:10.1109/TCIAIG.2010.2100395

Author McPartland, Michelle
Gallagher, Marcus
Title Reinforcement learning in first person shooter games
Journal name Ieee Transactions On Computational Intelligence and Ai in Games   Check publisher's open access policy
ISSN 1943-068X
Publication date 2011-03-01
Sub-type Article (original research)
DOI 10.1109/TCIAIG.2010.2100395
Open Access Status Not Open Access
Volume 3
Issue 1
Start page 43
End page 56
Total pages 14
Place of publication Piscataway, NJ, United States
Publisher I E E E
Language eng
Formatted abstract
Reinforcement learning (RL) is a popular machine learning technique that has many successes in learning how to play classic style games. Applying RL to first person shooter (FPS) games is an interesting area of research as it has the potential to create diverse behaviors without the need to implicitly code them. This paper investigates the tabular Sarsa (λ) RL algorithm applied to a purpose built FPS game. The first part of the research investigates using RL to learn bot controllers for the tasks of navigation, item collection, and combat individually. Results showed that the RL algorithm was able to learn a satisfactory strategy for navigation control, but not to the quality of the industry standard pathfinding algorithm. The combat controller performed well against a rule-based bot, indicating promising preliminary results for using RL in FPS games. The second part of the research used pretrained RL controllers and then combined them by a number of different methods to create a more generalized bot artificial intelligence (AI). The experimental results indicated that RL can be used in a generalized way to control a combination of tasks in FPS bots such as navigation, item collection, and combat.
Keyword Artificial intelligence (AI)
Computer games
Reinforcement learning (RL)
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2012 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 12 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 24 times in Scopus Article | Citations
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