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

Content Provider	IEEE Xplore Digital Library
Author	Hanna, C.J. Hickey, R.J. Charles, D.K. Black, M.M.
Copyright Year	2010
Description	Author affiliation: School of Computing and Information engineering, University of Ulster, Coleraine campus, Co. Londonderry, Northern Ireland, BT52 1SA, UK (Hanna, C.J.; Hickey, R.J.; Charles, D.K.; Black, M.M.)
Abstract	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.
Starting Page	380
Ending Page	387
File Size	518010
Page Count	8
File Format	PDF
ISBN	9781424462957
e-ISBN	9781424462964
DOI	10.1109/ITW.2010.5593329
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-18
Publisher Place	Denmark
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning Tiles Fires Games Computer architecture Energy states Space exploration
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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