By Topic

Modular Reinforcement Learning architectures for artificially intelligent agents in complex game environments

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Hanna, C.J. ; Sch. of Comput. & Inf. Eng., Univ. of Ulster, Coleraine, UK ; Hickey, R.J. ; Charles, D.K. ; Black, M.M.

Recently there has been much research focus on the use of Reinforcement Learning (RL) algorithms for game agent control. However, although it has been shown that such agents are capable of learning in real time, the high dimensionality of agent sensor state spaces still prove to be a significant barrier to progress. This paper outlines an approach to dealing with this issue by using a modular RL architecture with a fine granularity of modules. The modular approach enables a reduction of the dimensionality in complex game-like environments by dividing the state space into smaller, more manageable sub tasks. While this approach is successful in reducing dimensionality, challenges with action selection, exploration and reward allocation arise. This paper discusses approaches to overcoming these issues.

Published in:

Computational Intelligence and Games (CIG), 2010 IEEE Symposium on

Date of Conference:

18-21 Aug. 2010