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
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
A Comparison Study of Self-Adaptation in Evolution Strategies and Real-Coded Genetic Algorithms
Evolutionary Computation
A new co-mutation genetic operator
EC'08 Proceedings of the 9th WSEAS International Conference on Evolutionary Computing
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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A stochastic automaton can perform a finite number of actions in a random environment. When a specific action is performed, the environment responds by producing an environment output that is stochastically related to the action. The aim is to design an automaton, using an evolutionary reinforcement scheme (the basis of the learning process), that can determine the best action guided by past actions and responses. Using Stochastic Learning Automata techniques, we introduce a decision/control method for intelligent vehicles receiving data from on-board sensors or from the localization system of highway infrastructure.