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Rule Value Reinforcement Learning for Cognitive Agents

Child, C. H. T. & Stathis, K. (2006). Rule Value Reinforcement Learning for Cognitive Agents. Paper presented at the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS`06), 8 - 12 May 2006, Hakodate, Hokkaido, Japan. doi: 10.1145/1160633.1160773


RVRL (Rule Value Reinforcement Learning) is a new algorithm which extends an existing learning framework that models the environment of a situated agent using a probabilistic rule representation. The algorithm attaches values to learned rules by adapting reinforcement learning. Structure captured by the rules is used to form a policy. The resulting rule values represent the utility of taking an action if the rule`s conditions are present in the agent`s current percept. Advantages of the new framework are demonstrated, through examples in a predator-prey environment.

Publication Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science
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