Parallel reinforcement learning for weighted multi-criteria model with adaptive margin View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2009-03

AUTHORS

Kazuyuki Hiraoka, Manabu Yoshida, Taketoshi Mishima

ABSTRACT

Reinforcement learning (RL) for a linear family of tasks is described in this paper. The key of our discussion is nonlinearity of the optimal solution even if the task family is linear; we cannot obtain the optimal policy using a naive approach. Although an algorithm exists for calculating the equivalent result to Q-learning for each task simultaneously, it presents the problem of explosion of set sizes. We therefore introduce adaptive margins to overcome this difficulty. More... »

PAGES

17-24

Journal

TITLE

Cognitive Neurodynamics

ISSUE

1

VOLUME

3

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11571-008-9066-9

DOI

http://dx.doi.org/10.1007/s11571-008-9066-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1043929226

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/19003453


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