Presentation	2012-06-19 Improving the Variable Setting of Softmax Selection From an Information-Theoretic Viewpoint Kazunori IWATA,
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Abstract(in English)	We focus on softmax selection which is the most popular description of the policy for action selection in reinforcement learning. Compared with other sophisticated methods in the literature, it is easy to implement and simple because there is essentially only one parameter that needs to be tuned. Moreover, it is often adequate in practice when the parameter is set appropriately for the environment. In this paper, we improve its variable setting to extend the bandwidth around the best parameter so that we can save time and cost in the implementation and parameter-tuning. Using various types of tasks, we show that our setting is effective in extending the bandwidth.
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Keyword(in English)	reinforcement learning / softmax selection / information theory
Paper #	IBISML2012-4
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Committee	IBISML
Conference Date	2012/6/12(1days)
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Paper Information
Registration To	Information-Based Induction Sciences and Machine Learning (IBISML)
Language	ENG
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Improving the Variable Setting of Softmax Selection From an Information-Theoretic Viewpoint
Sub Title (in English)
Keyword(1)	reinforcement learning
Keyword(2)	softmax selection
Keyword(3)	information theory
1st Author's Name	Kazunori IWATA
1st Author's Affiliation	Graduate School of Information Sciences, Hiroshima City University()
Date	2012-06-19
Paper #	IBISML2012-4
Volume (vol)	vol.112
Number (no)	83
Page	pp.pp.-
#Pages	8
Date of Issue