Learning automata: an introduction
Learning automata: an introduction
Simulation study of multiple intelligent vehicle control using stochastic learning automata
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
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
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
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
Reinforcement learning estimation of distribution algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
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
Generic reinforcement schemes and their optimization
ECC'11 Proceedings of the 5th European conference on European computing conference
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Using Stochastic Learning Automata, we can build robust learning systems without the complete knowledge of their environments. A Stochastic Learning Automaton is a learning entity that learns the optimal action to use from its set of possible actions. The algorithm that guarantees the desired learning process is called a reinforcement scheme. A major advantage of reinforcement learning compared to other learning approaches is that it requires no information about the environment except for the reinforcement signal. The drawback is that a reinforcement learning system is slower than other approaches for most applications since every action needs to be tested a number of times for a good performance. In our approach, the learning process must be much faster than the environment changes, and for accomplish this we need efficient reinforcement schemes. The aim of this paper is to present a reinforcement scheme which satisfies all necessary and sufficient conditions for absolute expediency for a stationary environment. Our scheme provides better results, compared with other nonlinear reinforcement schemes. Furthermore, using a Breeder genetic algorithm, we are providing the optimal learning parameters for our scheme, in order to reach the best performance.