Goal-Directed Online Learning of Predictive Models

We present an algorithmic approach for integrated learning and planning in predictive representations. The approach extends earlier work on predictive state representations to the case of online exploration, by allowing exploration of the domain to proceed in a goal-directed fashion and thus be more efficient. Our algorithm interleaves online learning of the models, with estimation of the value function. The framework is applicable to a variety of important learning problems, including scenarios such as apprenticeship learning, model customization, and decision-making in non-stationary domains.