Online Learning and Exploiting Relational Models in Reinforcement Learning
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 726-730.
Abstract
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.
@inproceedings{ijcai2007p116,
title = {Online Learning and Exploiting Relational Models in Reinforcement Learning},
author = {Croonenborghs, Tom and Ramon, Jan and Blockeel, Hendrik and Bruynooghe, Maurice},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {726--730},
year = {2007},
url = {https://ijcai.org/proceedings/2007/116},
}
TY - CPAPER TI - Online Learning and Exploiting Relational Models in Reinforcement Learning AU - Croonenborghs, Tom AU - Ramon, Jan AU - Blockeel, Hendrik AU - Bruynooghe, Maurice T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 726 EP - 730 UR - https://ijcai.org/proceedings/2007/116 ER -Copied
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