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Modular Reinforcement Learning Architectures for Artificially Intelligent Agents in Complex Game Environments

Hanna, Christopher J, Hickey, RJ, Charles, DK and Black, Michaela (2010) Modular Reinforcement Learning Architectures for Artificially Intelligent Agents in Complex Game Environments. In: IEEE Computational Intelligence and Games Conference, Copenhagen. IEEE. 8 pp. [Conference contribution]

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DOI: 10.1109/ITW.2010.5593329


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.keywords: {Computer architecture;Energy states;Fires;Games;Learning;Space exploration;Tiles;computer games;learning (artificial intelligence);multi-agent systems;agent sensor state spaces;artificially intelligent agents;complex game environments;game agent control;modular reinforcement learning architectures;}

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Information and Communication Engineering
ID Code:24857
Deposited By: Dr Darryl Charles
Deposited On:07 Feb 2013 10:18
Last Modified:07 Feb 2013 10:18

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