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Reseach Article

Exploration and Exploitation Tradeoff using Fuzzy Reinforcement Learning

by Seyed Mohammad Hossein Nabavi, Somayeh Hajforoosh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 59 - Number 5
Year of Publication: 2012
Authors: Seyed Mohammad Hossein Nabavi, Somayeh Hajforoosh
10.5120/9545-3994

Seyed Mohammad Hossein Nabavi, Somayeh Hajforoosh . Exploration and Exploitation Tradeoff using Fuzzy Reinforcement Learning. International Journal of Computer Applications. 59, 5 ( December 2012), 26-31. DOI=10.5120/9545-3994

@article{ 10.5120/9545-3994,
author = { Seyed Mohammad Hossein Nabavi, Somayeh Hajforoosh },
title = { Exploration and Exploitation Tradeoff using Fuzzy Reinforcement Learning },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 59 },
number = { 5 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 26-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume59/number5/9545-3994/ },
doi = { 10.5120/9545-3994 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:05:21.785783+05:30
%A Seyed Mohammad Hossein Nabavi
%A Somayeh Hajforoosh
%T Exploration and Exploitation Tradeoff using Fuzzy Reinforcement Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 59
%N 5
%P 26-31
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Difficulty of making a balance between exploration and exploitation in multiagent environment is a dilemma that does not have a clear answer and there are still different methods for investigation of this problem that all refer to it. In this paper, we provide a method based on fuzzy variables for making exploration and exploitation in multiagent environment. In this method, an effective agent (? in ?-greedy method) is obtained which is updated using fuzzy variables in each step to manage tradeoff between exploration and exploitation. The proposed algorithm is investigated for determination an optimized path in the Grid World. In this method, agents effort to reach locations with a highest gain in a cooperative environment. Outcomes of the suggested fuzzy based algorithm compared with the results by conventional ?-greedy method. In addition, quality improvement of interaction between exploration and exploitation is discussed.

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Index Terms

Computer Science
Information Sciences

Keywords

Reinforcement learning Multiagent environment Balance between exploration and exploitation Q-Learning