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Improving the Learning Rate by Inducing a Transition Model

Bridle, Robert; McCreath, Eric


In general, a reinforcement learning agent requires many trials in order to find a successful policy in a domain. In this paper we investigate inducing a transition model to reduce the number of trials required by an agent. We discuss an approach that incorporates transition model learning within a contemporary agent design.

CollectionsANU Research Publications
Date published: 2004
Type: Conference paper
Source: Proceedings of the Third International Joint Conference on Autonomous Agents & Multi Agent Systems (AAMAS 2004)


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