Abstract
Finding the Bayesian balance between exploration and exploitation in adaptive optimal control is in general intractable. This paper shows how to compute suboptimal estimates based on a certainty equivalence approximation (Cozzolino, Gonzalez-Zubieta & Miller, 1965) arising from a form of dual control. This system-atizes and extends existing uses of exploration bonuses in reinforcement learning (Sutton, 1990). The approach has two components: a statistical model of uncertainty in the world and a way of turning this into exploratory behavior. This general approach is applied to two-dimensional mazes with moveable barriers and its performance is compared with Sutton's DYNA system.
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