NDLI: Proposal and evaluation of the penalty avoiding rational policy making algorithm with penalty level

Content Provider	IEEE Xplore Digital Library
Author	Miyazaki, K. Kojima, T. Kobayashi, H.
Copyright Year	2007
Description	Author affiliation: Nat. Instn. for Acad. Degrees & Univ. Evaluation, Tokyo (Miyazaki, K.)
Abstract	Reinforcement learning (RL) is a kind of machine learning. It aims to adapt an agent to a given environment by utilizing a reward and a penalty. We know the penalty avoiding rational policy making algorithm (PARP) and the penalty avoiding profit sharing (PAPS) as examples of RL systems that are able to suppress a penalty and learn a rational policy. However they cannot treat multiple penalties. In this paper, we extend PARP/PAPS to the environments where there are some kinds of penalties. We propose the penalty avoiding rational policy making algorithm with penalty level $(PARP_{L})$ that can control how to avoid penalties. We show the effectiveness of $PARP_{L}$ by soccer game simulations.
Starting Page	2766
Ending Page	2773
File Size	751524
Page Count	8
File Format	PDF
ISBN	9784907764272
DOI	10.1109/SICE.2007.4421459
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-17
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	SICE
Subject Keyword	Proposals Machine learning algorithms Electronic mail Machine learning Dynamic programming soccer game Reinforcement Learning Reward and Penalty Penalty Avoiding Rational Policy Making algorithm Profit Sharing
Content Type	Text
Resource Type	Article

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Proposal and evaluation of the penalty avoiding rational policy making algorithm with penalty level