Associative reinforcement learning of real-valued functions

The author describes an algorithm, called the stochastic real-valued (SRV) algorithm, that uses evaluative performance feedback to learn associative maps from input vectors to real-valued actions. This algorithm is based on the pioneering work of A.G. Barto and P. Anandan (1985), in synthesizing associative reinforcement learning (ARL) algorithms using techniques from pattern classification and automata theory. A strong convergence theorem is presented that implies a form of optimal performance under certain general conditions of the SRV algorithm on ARL tasks. Simulation results are presented to illustrate the convergence behavior of the algorithm under the conditions of the theorem. The robustness of the algorithm is also demonstrated by simulations in which some of the conditions of the theorem are violated