Proceedings of the Annual Meeting of the Cognitive Science Society

Reinforcement Learning methods address the prob- lem faced by an agent w h o must choose actions in an u n k n o w n environment so as to maximize the re- wards it receives in return. T o date, the available techniques have relied on temporal discounting, a problematic practice of valuing immediate rewards more heavily than future rewards, or else have im- posed strong restrictions on the environment. This paper sketches a n e w method which utilizes a subjec- tive evaluator of performance in order to (1) choose actions that maximize undiscounted rewards and (2) do so at a computational advantage with respect to previous discounted techniques. W e present initial experimental results that attest to a substantial im- provement in performance.