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IJCAI
2007

Online Learning and Exploiting Relational Models in Reinforcement Learning

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Online Learning and Exploiting Relational Models in Reinforcement Learning
In recent years, there has been a growing interest in using rich representations such as relational languages for reinforcement learning. However, while expressive languages have many advantages in terms of generalization and reasoning, extending existing approaches to such a relational setting is a non-trivial problem. In this paper, we present a first step towards the online learning and exploitation of relational models. We propose a representation for the transition and reward function that can be learned online and present a method that exploits these models by augmenting Relational Reinforcement Learning algorithms with planning techniques. The benefits and robustness of our approach are evaluated experimentally.
Tom Croonenborghs, Jan Ramon, Hendrik Blockeel, Ma
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where IJCAI
Authors Tom Croonenborghs, Jan Ramon, Hendrik Blockeel, Maurice Bruynooghe
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