- Title
- A learning classifier system approach to relational reinforcement learning
- Creator
- Mellor, Drew
- Relation
- Learning Classifier Systems: 10th International Workshop, IWLCS 2006, Seattle, MA, USA, July 8, 2006, and 11th International Workshop, IWLCS 2007, London, UK, July 8, 2007: Revised Selected Papers p. 169-188
- Relation
- Lecture Notes in Artificial Intelligence 4998
- Publisher Link
- http://dx.doi.org/10.1007/978-3-540-88138-4
- Publisher
- Springer
- Resource Type
- book chapter
- Date
- 2008
- Description
- This article describes a learning classifier system (LCS) approach to relational reinforcement learning (RRL). The system, Foxcs-2, is a derivative of Xcs that learns rules expressed as definite clauses over first-order logic. By adopting the LCS approach, Foxcs-2, unlike many RRL systems, is a general, model-free and “tabula rasa” system. The change in representation from bit-strings in Xcs to first-order logic in Foxcs-2 necessitates modifications, described within, to support matching, covering, mutation and several other functions. Evaluation on inductive logic programming (ILP) and RRL tasks shows that the performance of Foxcs-2 is comparable to other systems. Further evaluation on RRL tasks highlights a significant advantage of Foxcs-2’s rule language: in some environments it is able to represent policies that are genuinely scalable; that is, policies that are independent of the size of the environment.
- Subject
- learning classifier system; Foxcs-2; inductive logic programming; relational reinforcement learning; policies
- Identifier
- http://hdl.handle.net/1959.13/804720
- Identifier
- uon:6707
- Identifier
- ISBN:9783540881377
- Language
- eng
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