By Topic

Learning automata with changing number of actions

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
M. A. L. Thathachar ; Dept. of Electr. Eng., Indian Inst. of Sci., Bangalore, India ; Bhaskar R. Harita

A reinforcement scheme that is based on the linear reward-inaction updating algorithm is presented for a learning automaton whose action set changes from instant to instant. A learning automaton using the algorithm is shown to be both absolutely expedient and ε-optimal. The simulation results verify the ε-optimality of the algorithm. The results can be extended to the design of general nonlinear absolutely expedient learning algorithms.

Published in:

IEEE Transactions on Systems, Man, and Cybernetics  (Volume:17 ,  Issue: 6 )