A Learning Automaton Network with Variable Random Environment

Abstract

There exist many types of cooperativity in the mathematical models and computer simulations of neural networks. In a previous paper we presented a reinforcement learning network with a different type of cooperativity from that of neural networks, using self-interested learning automata. In this paper we generalize our previous reinforcement learning algorithm in order to solve some learning problems with variable random environments, and also present a new algorithm based on Bayes decision theory. Simulation results show how cooperative activity in reinforcement learning networks can solve learning control problems.