A new nonlinear reinforcement scheme for stochastic learning automata
Dana Simian, Florin Stoica · 2010
Reinforcement schemes represent the basis of the learning process for stochastic learning automata, generating their learning behavior. An automaton using a reinforcement scheme can decide the best action, based on past actions and environment responses. The aim of this paper is to introduce a new reinforcement scheme for stochastic learning automata. We test our schema and compare with other nonlinear reinforcement schemes. The results reveal a faster convergence of the new schema to the optimal action.