Bellman goes Relational. Kersting, K., Otterlo, M. V., & Raedt, L. D. Proceedings of the Twenty-First International Conference on Machine Learning (ICML-2004), 2004. abstract bibtex 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.
@article{Kersting:2004,
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.},
author = {Kersting, Kristian and Otterlo, Martijn Van and Raedt, Luc De},
interhash = {640ed3c7c9747d4da813c1bff4eaf47e},
intrahash = {6811a3df8d0e864d523b5d6263262dcb},
journal = {Proceedings of the Twenty-First International Conference on Machine
Learning ({ICML}-2004)},
pages = {465-472},
title = {Bellman goes Relational},
year = 2004
}
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