  We introduce a framework for model learning and planning in stochastic
domains with continuous state and action spaces and non-Gaussian transition
models. It is efficient because (1) local models are estimated only when the
planner requires them; (2) the planner focuses on the most relevant states to
the current planning problem; and (3) the planner focuses on the most
informative and/or high-value actions. Our theoretical analysis shows the
validity and asymptotic optimality of the proposed approach. Empirically, we
demonstrate the effectiveness of our algorithm on a simulated multi-modal
pushing problem.
