NDLI: Novelty-organizing team of classifiers - A team-individual multi-objective approach to reinforcement learning

Content Provider	IEEE Xplore Digital Library
Author	Vargas, D.V. Takano, H. Murata, J.
Copyright Year	2014
Description	Author affiliation: Grad. Sch. of Inf. Sci. & Electr. Eng., Kyushu Univ., Fukuoka, Japan (Vargas, D.V.) \|\| Fac. of Inf. Sci. & Electr. Eng., Kyushu Univ., Fukuoka, Japan (Takano, H.; Murata, J.)
Abstract	In reinforcement learning, there are basically two spaces to search: value-function space and policy space. Consequently, there are two fitness functions each with their associated trade-offs. However, the problem is still perceived as a single-objective one. Here a multi-objective reinforcement learning algorithm is proposed with a structured novelty map population evolving feedforward neural models. It outperforms a gradient based continuous input-output state-of-art algorithm in two problems. Contrary to the gradient based algorithm, the proposed one solves both problems with the same parameters and smaller variance of results. Moreover, the results are comparable even with other discrete action algorithms of the literature as well as neuroevolution methods such as NEAT. The proposed method brings also the novelty map population concept, i.e., a novelty map-based population which is less sensitive to the input distribution and therefore more suitable to create the state space. In fact, the novelty map framework is shown to be less dynamic and more resource efficient than variants of the self-organizing map.
Starting Page	1785
Ending Page	1792
File Size	1276798
Page Count	8
File Format	PDF
e-ISBN	9784907764463
DOI	10.1109/SICE.2014.6935299
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-09
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	SICE
Subject Keyword	Learning (artificial intelligence) Sociology Statistics Arrays System-on-chip Educational institutions Heuristic algorithms Novelty-Organizing Classifiers Novelty Self Organizing Map Learning Classifier Systems Reinforcement Learning Structured Evolutionary Algorithms Novelty Map
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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