Absorbing and ergodic discretized two-action learning automata
IEEE Transactions on Systems, Man and Cybernetics
Learning automata: an introduction
Learning automata: an introduction
Continuous Learning Automata Solutions to the Capacity Assignment Problem
IEEE Transactions on Computers
Learning Algorithms Theory and Applications
Learning Algorithms Theory and Applications
Graph Partitioning Using Learning Automata
IEEE Transactions on Computers
Adaptation of Parameters of BP Algorithm Using Learning Automata
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
The Bayesian pursuit algorithm: a new family of estimator learning automata
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
String taxonomy using learning automata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multiple stochastic learning automata for vehicle path control in an automated highway system
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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The success of Learning Automata (LA)-based estimator algorithms over the classical, Linear Reward-Inaction (LRI)-like schemes, can be explained by their ability to pursue the actions with the highest reward probability estimates. Without access to reward probability estimates, it makes sense for schemes like the LRI to first make large exploring steps, and then to gradually turn exploration into exploitation by making progressively smaller learning steps. However, this behavior becomes counter-intuitive when pursuing actions based on their estimated reward probabilities. Learning should then ideally proceed in progressively larger steps, as the reward probability estimates turn more accurate. This paper introduces a new estimator algorithm, the Discretized Bayesian Pursuit Algorithm (DBPA), that achieves this. The DBPA is implemented by linearly discretizing the action probability space of the Bayesian Pursuit Algorithm (BPA) [1]. The key innovation is that the linear discrete updating rules mitigate the counter-intuitive behavior of the corresponding linear continuous updating rules, by augmenting them with the reward probability estimates. Extensive experimental results show the superiority of DBPA over previous estimator algorithms. Indeed, the DBPA is probably the fastest reported LA to date.