Relative reward strength algorithms for learning automata

A novel class of action probability update algorithms for learning automata that use the relative reward strengths of responses from the environment is examined. Specifically, update algorithms for S-model automata in which `recent´ environmental responses for each of the actions retained are used. A convergence result is proven and the behavior of these automat is studied by simulation. A major result is that the performance of these algorithms is superior, in several respects, to that of the well-known SL_R-1 update algorithm. Additional results are presented on the variability of performance, the cost of learning and, in the case of static environments, modifications that result in improved convergence