Learning automata: an introduction
Learning automata: an introduction
Incremental reinforcement learning for designing multi-agent systems
Proceedings of the fifth international conference on Autonomous agents
New Topics in Learning Automata Theory and Applications
New Topics in Learning Automata Theory and Applications
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Intelligent navigation of autonomous vehicles in an automated highway system: learning methods and interacting vehicles approach
The science of breeding and its application to the breeder genetic algorithm (bga)
Evolutionary Computation
Automatic control based on wasp behavioral model and stochastic learning automata
MAMECTIS'08 Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
A new nonlinear reinforcement scheme for stochastic learning automata
ACMOS'10 Proceedings of the 12th WSEAS international conference on Automatic control, modelling & simulation
Optimizing a new nonlinear reinforcement scheme with Breeder genetic algorithm
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
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The aim of this paper is to introduce a generic two-parameters dependent absolutely expedient reinforcement scheme and to present a method for learning parameters optimization. We optimize, using a Breeder genetic algorithm, many schemes derived from our generic one, in order to reach the best performance. Furthermore, we compare our results in terms of speed and efficiency.