A new evolutionary reinforcement scheme for stochastic learning automata
Florin Stoica, Emil M. Popa · Annual Conference on Computers · 2008
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.