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
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. This response may be favourable or unfavourable. The aim is to design an automaton 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, in an infrastructure managed architecture. The aim is to design an automata system that can learn the best possible action based on the data received from on-board sensors or from the localization system of highway infrastructure.