Bellman goes relational

Authors:
Kristian Kersting;Martijn Van Otterlo;Luc De Raedt
Affiliations:
University of Freiburg, Freiburg, Germany;University of Freiburg, Freiburg, Germany;University of Freiburg, Freiburg, Germany
Venue:
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Year:
2004

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Abstract

Motivated by the interest in relational reinforcement learning, we introduce a novel relational Bellman update operator called REBEL. It employs a constraint logic programming language to compactly represent Markov decision processes over relational domains. Using REBEL, a novel value iteration algorithm is developed in which abstraction (over states and actions) plays a major role. This framework provides new insights into relational reinforcement learning. Convergence results as well as experiments are presented.