Focused Model-Learning and Planning for Non-Gaussian Continuous   State-Action Systems
  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. 
Zi Wang, Stefanie Jegelka, Leslie Pack Kaelbling, Tom\'as   Lozano-P\'erez
cs.AI cs.LG cs.RO stat.AP stat.ML