Relational Representations in Reinforcement Learning: Review and Open Problems

M. van Otterlo

Relational Representations in Reinforcement Learning: Review and Open Problems

M. van Otterlo

Research output: Contribution to conference › Paper › Academic › peer-review

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Abstract

This paper is about representation in RL.We discuss some of the concepts in representation and generalization in reinforcement learning and argue for higher-order representations, instead of the commonly used propositional representations. The paper contains a small review of current reinforcement learning systems using higher-order representations, followed by a brief discussion. The paper ends with research directions and open problems.

Original language	English
Number of pages	8
Publication status	Published - 9 Jul 2002
Event	ICML-2002 Workshop on Development of Representations - University of New South Wales, Sydney, Australia Duration: 9 Jul 2002 → 9 Jul 2002 http://www.demo.cs.brandeis.edu/icml02ws/

Workshop

Workshop	ICML-2002 Workshop on Development of Representations
Country	Australia
City	Sydney
Period	9/07/02 → 9/07/02
Internet address	http://www.demo.cs.brandeis.edu/icml02ws/

Keywords

EWI-14945
Review
relational representations
HMI-IA: Intelligent Agents
IR-65334
Generalization
Reinforcement learning

Access to Document

VanOtterlo2002relational
open
Final published version, 138 KB

http://demo.cs.brandeis.edu/icml02ws/otterlo.pdf

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