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Learning and evolving combat game controllers

Peña, L and Ossowski, S and Peña, JM and Lucas, SM (2012) Learning and evolving combat game controllers. In: UNSPECIFIED, ? - ?.

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Abstract

The design of the control mechanisms for the agents in modern video games is one of the main tasks involved in the game design process. Designing controllers grows in complexity as either the number of different game agents or the number of possible actions increase. An alternative mechanism to hard-coding agent controllers is the use of learning techniques. This paper introduces two new variants of a hybrid algorithm, named WEREWoLF and WERESARSA, that combine evolutionary techniques with reinforcement learning. Both new algorithms allow a group of different reinforcement learning controllers to be recombined in an iterative process that uses both evolution and learning. These new algorithms have been tested against different instances of predefined controllers on a one-on-one combat simulator, with underlying game mechanics similar to classic arcade games of this kind. The results have been compared with other reinforcement learning controllers, showing that WEREWoLF outperforms the other algorithms for a series of different learning conditions. © 2012 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2012 IEEE Conference on Computational Intelligence and Games, CIG 2012
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 19 Oct 2012 22:56
Last Modified: 23 Jan 2019 02:15
URI: http://repository.essex.ac.uk/id/eprint/4126

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