Journal ArticleDOI
On a general class of absorbing-barrier learning algorithms
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TLDR
Necessary and sufficient conditions for a general class of learning algorithms to be an absorbing-barrier algorithm are given and examples of absorbing- barrier learning algorithms whose asymptotic behavior is so far unknown are described.About:
This article is published in Information Sciences.The article was published on 1981-08-01. It has received 6 citations till now. The article focuses on the topics: Stability (learning theory) & Weighted Majority Algorithm.read more
Citations
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Journal ArticleDOI
ε-Optimality of a general class of learning algorithms
TL;DR: Conditions of e-optimality of a general class of strongly absolutely expedient learning algorithms are derived and are applied to reinforcement learning.
Book ChapterDOI
Introduction to Learning Automata Models
Alireza Rezvanian,Behnaz Moradabadi,Mina Ghavipour,Mohammad Mehdi Daliri Khomami,Mohammad Reza Meybodi +4 more
TL;DR: In this chapter, learning automaton and suitable variants of LA models for distributed and decentralized environments (e.g., social networks) will be introduced and recent models and applications of learning automata will be presented.
Proceedings ArticleDOI
Using Learning Automata in Cooperation among Agents in a Team
TL;DR: A new technique called the "best corner in state square" is introduced and designed for generalizing the vast number of states in agent's domain environment to a few number ofStates by building a virtual grid in that environment.
Book ChapterDOI
On a Class of Learning Algorithms with Symmetric Behavior under Success and Failure
TL;DR: The authors' interests are in the latter approach of Learning Automata Theory and Mathematical Statistics, where the aim is to build algorithms that exhibit prespecified behaviour.
References
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Journal ArticleDOI
Stochastic Models for Learning. By Robert R. Bush and Frederick Mosteller. Pp. xvi, 365. 72s. 1955. (John Wiley and Sons, New York; Chapman and Hall, London)
Journal ArticleDOI
Learning Automata - A Survey
TL;DR: Attention has been focused on the norms of behavior of learning automata, issues in the design of updating schemes, convergence of the action probabilities, and interaction of several automata.