Reinforcement Learning for Relational MDPs
Otterlo van, Martijn (2004) Reinforcement Learning for Relational MDPs. In: Machine Learning Conference of Belgium and the Netherlands, BeNeLearn '04, 8-9 Jan 2004, Brussels, Belgium.
|Abstract:||In this paper we present a new method for reinforcement learning in relational domains. A logical language is employed to abstract over states and actions, thereby decreasing the size of the state-action space significantly. A probabilistic transition model of the abstracted Markov-Decision-Process is estimated to to speed-up learning. We present theoretical and experimental analysis of our new representation. Some insights concerning the problems and opportunities of logical representations for reinforcement learning are obtained in the context of a growing interest in the use of abstraction in reinforcement learning contexts.
|Item Type:||Conference or Workshop Item|
Electrical Engineering, Mathematics and Computer Science (EEMCS)
|Link to this item:||http://purl.utwente.nl/publications/64849|
|Export this item as:||BibTeX|
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