ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

A Hierarchical System of Learning Automata

Thathachar, MAL and Ramakrishnan, KR (1981) A Hierarchical System of Learning Automata. In: IEEE Transactions On Systems, Man, And Cybernetics,, 11 (3). pp. 236-241.

[img] PDF
getPDF.pdf - Published Version
Restricted to Registered users only

Download (1MB) | Request a copy
Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumb...


A learning automaton operating in a random environment updates its action probabilities on the basis of the reactions of the environment, so that asymptotically it chooses the optimal action. When the number of actions is large the automaton becomes slow because there are too many updatings to be made at each instant. A hierarchical system of such automata with assured c-optimality is suggested to overcome that problem.The learning algorithm for the hierarchical system turns out to be a simple modification of the absolutely expedient algorithm known in the literature. The parameters of the algorithm at each level in the hierarchy depend only on the parameters and the action probabilities of the previous level. It follows that to minimize the number of updatings per cycle each automaton in the hierarchy need have only two or three actions.

Item Type: Journal Article
Publication: IEEE Transactions On Systems, Man, And Cybernetics,
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Additional Information: Copyright 1981 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 12 Aug 2009 05:00
Last Modified: 19 Sep 2010 05:37
URI: http://eprints.iisc.ac.in/id/eprint/21488

Actions (login required)

View Item View Item