NDLI: Reinforcement learning approach to multi-stage decision making problems with changes in action sets

Content Provider	Springer Nature Link
Author	Etoh, Takuya Taka, Hirotaka Murata, Junichi
Copyright Year	2012
Abstract	Multi-stage decision making (MSDM) problems often include changes in practical situations. For example, in the shortest route selection problems in road networks, travelling times of road sections vary depending on traffic conditions. The changes give rise to risks in adopting particular solutions to MSDM problems. Therefore, a method is proposed in this paper for solving MSDM problems considering the risks. Reinforcement learning (RL) is adopted as a method for solving those problems, and stochastic changes of action sets are treated. It is necessary to evaluate risks based on subjective views of decision makers (DMs) because the risk evaluation is by nature subjective and depends on DMs. Therefore, an RL approach is proposed which uses a new method for evaluating risks of the changes that can easily incorporate the DM’s subjective view and can be readily imbedded in reinforcement learning algorithms. The effectiveness of the method is illustrated with a road network path selection problem.
Starting Page	293
Ending Page	299
Page Count	7
File Format	PDF
ISSN	14335298
Journal	Artificial Life and Robotics
Volume Number	17
Issue Number	2
e-ISSN	16147456
Language	English
Publisher	Springer Japan
Publisher Date	2012-11-06
Publisher Place	Japan
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multi-stage decision making Risk assessment Subjective occurrence probability Reinforcement learning Artificial Intelligence (incl. Robotics) Computation by Abstract Devices Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Biochemistry, Genetics and Molecular Biology

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