Publication Type

Journal Article

Version

acceptedVersion

Publication Date

7-2014

Abstract

Games are good test-beds to evaluate AI methodologies. In recent years, there has been a vast amount of research dealing with real-time computer games other than the traditional board games or card games. This paper illustrates how we create agents by employing FALCON, a self-organizing neural network that performs reinforcement learning, to play a well-known first-person shooter computer game called Unreal Tournament. Rewards used for learning are either obtained from the game environment or estimated using the temporal difference learning scheme. In this way, the agents are able to acquire proper strategies and discover the effectiveness of different weapons without any guidance or intervention. The experimental results show that our agents learn effectively and appropriately from scratch while playing the game in real-time. Moreover, with the previously learned knowledge retained, our agent is able to adapt to a different opponent in a different map within a relatively short period of time.

Keywords

Reinforcement learning, real-time computer game, Unreal Tournament, Adaptive Resonance Theory operations, temporal difference learning

Discipline

Artificial Intelligence and Robotics | Computer Engineering

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Computational Intelligence and AI in Games

Volume

7

Issue

2

First Page

123

Last Page

138

ISSN

1943-068X

Identifier

10.1109/TCIAIG.2014.2336702

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Additional URL

https://doi.org/10.1109/TCIAIG.2014.2336702

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