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Authors: Robert Wright 1 ; Steven Loscalzo 2 and Lei Yu 3

Affiliations: 1 Air Force Research Lab, United States ; 2 Air Force Research Lab and Binghamton University, United States ; 3 Binghamton University, United States

Keyword(s): Reinforcement learning, Feature selection, Neuroevolution.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Autonomous Systems ; Bioinformatics ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Information Systems Analysis and Specification ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Methodologies and Technologies ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Operational Research ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Simulation ; Soft Computing ; Symbolic Systems ; Theory and Methods

Abstract: Classical reinforcement learning techniques become impractical in domains with large complex state spaces. The size of a domain’s state space is dominated by the number of features used to describe the state. Fortunately, in many real-world environments learning an effective policy does not usually require all the provided features. In this paper we present a feature selection algorithm for reinforcement learning called Incremental Feature Selection Embedded in NEAT (IFSE-NEAT) that incorporates sequential forward search into neuroevolutionary algorithm NEAT. We provide an empirical analysis on a realistic simulated domain with many irrelevant and relevant features. Our results demonstrate that IFSE-NEAT selects smaller and more effective feature sets than alternative approaches, NEAT and FS-NEAT, and superior performance characteristics as the number of available features increases.

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Paper citation in several formats:
Wright, R.; Loscalzo, S. and Yu, L. (2011). EMBEDDED INCREMENTAL FEATURE SELECTION FOR REINFORCEMENT LEARNING. In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-8425-40-9; ISSN 2184-433X, SciTePress, pages 263-268. DOI: 10.5220/0003153402630268

@conference{icaart11,
author={Robert Wright. and Steven Loscalzo. and Lei Yu.},
title={EMBEDDED INCREMENTAL FEATURE SELECTION FOR REINFORCEMENT LEARNING},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2011},
pages={263-268},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003153402630268},
isbn={978-989-8425-40-9},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - EMBEDDED INCREMENTAL FEATURE SELECTION FOR REINFORCEMENT LEARNING
SN - 978-989-8425-40-9
IS - 2184-433X
AU - Wright, R.
AU - Loscalzo, S.
AU - Yu, L.
PY - 2011
SP - 263
EP - 268
DO - 10.5220/0003153402630268
PB - SciTePress