Varieties of learning automata: an overview

2012

Wireless Pers Commun

In wireless sensor networks, when each target is covered by multiple sensors, we can schedule sensor nodes to monitor deployed targets in order to improve lifetime of network. In this paper, we propose an efficient scheduling method based on learning automata, in which each node is equipped with a learning automaton, which helps the node to select its proper state (active or sleep), at any given time. To study the performance of the proposed method, computer simulations are conducted. Results of these simulations show that the proposed scheduling method can better prolong the lifetime of the network in comparison to similar existing methods.

Section: Related Workmentioning

confidence: 99%

Maximizing Lifetime of Target Coverage in Wireless Sensor Networks Using Learning Automata

Mostafaei

2012

Wireless Pers Commun

“…The linear reward-penalty algorithm (L R-P ) is one of the various learning algorithms reported in the literature [39]. Let α i be the action chosen at time k as a sample realization from distribution p(k).…”

Section: Learning Automatamentioning

confidence: 99%

A note on the learning automata based algorithms for adaptive parameter selection in PSO

Hashemi

2011

Applied Soft Computing

PSO, like many stochastic search methods, is very sensitive to efficient parameter setting such that modifying a single parameter may cause a considerable change in the result. In this paper, we study the ability of learning automata for adaptive PSO parameter selection. We introduced two classes of learning automata based algorithms for adaptive selection of value for inertia weight and acceleration coefficients. In the first class, particles of a swarm use the same parameter values adjusted by learning automata. In the second class, each particle has its own characteristics and sets its parameter values individually. In addition, for both classed of proposed algorithms, two approaches for changing value of the parameters has been applied. In first approach, named adventurous, value of a parameter is selected from a finite set while in the second approach, named conservative, value of a parameter either changes by a fixed amount or remains unchanged. Experimental results show that proposed learning automata based algorithms compared to other schemes such as SPSO, PSOIW, PSO-TVAC, PSOLP, DAPSO, GPSO, and DCPSO have the same or even higher ability to find better solutions. In addition, proposed algorithms converge to stopping criteria for some of the highly multi modal functions significantly faster.

“…For more information on learning automata, the reader may refer to (Thathachar & Sastry, 2002;Thathachar & Sastry, 2004).…”

Section: Learning Automata Learning Automata Games and Entropymentioning

confidence: 99%

Speeding up learning automata based multi agent systems using the concepts of stigmergy and entropy

Masoumi

Expert Systems with Applications

2011

a b s t r a c tLearning automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms and are able to control the stochastic games. In this paper, the concepts of stigmergy and entropy are imported into learning automata based multi-agent systems with the purpose of providing a simple framework for interaction and coordination in multi-agent systems and speeding up the learning process. The multi-agent system considered in this paper is designed to find optimal policies in Markov games. We consider several dummy agents that walk around in the states of the environment, make local learning automaton active, and bring information so that the involved learning automaton can update their local state. The entropy of the probability vector for the learning automata of the next state is used to determine reward or penalty for the actions of learning automata. The experimental results have shown that in terms of the speed of reaching the optimal policy, the proposed algorithm has better learning performance than other learning algorithms.