A new approach to the design of reinforcement schemes for learning automata: stochastic estimator learning algorithms

A new approach to the design of S-model ergodic reinforcement learning algorithms is introduced. The new scheme utilizes a stochastic estimator and can operate in nonstationary environments with high accuracy and high adaptation rate. The performance of the presented stochastic estimator learning automation (SELA) is superior over previous well-known S-model ergodic schemes. Furthermore it is proved that SELA is ε-optimal in every S-model random environment