Skill Learning

1. Environment Environment Supplied Control Policy Supplied Control Policy Planning Skill Learning Built-in Behaviors Phase One Phase Two R O A R O A Learning System Learning System Adaptive Intelligent Mobile RoboticsLeslie Pack Kaelbling, PIMIT Artificial Intelligence Laboratory Hierarchical Domain Decomposition for Probabilistic Planning • High-level goal determines reward at exit states • Combine pre-computed value functions to determine near-optimal action • Construct decomposition off line • Solve for macro operators • Plan for new goals in time logarithmic in plan length • Trade optimality for efficiency Vision-Based Navigation Practical Reinforcement Learning • Human guidance generates efficient exploration • Locally weighted regression provides fast function approximation • Uncertainty modeling and experience replay cause fast value propagation Optical flow gives estimated distance to objects Comparison of potential-field method to empirically discovered human control laws for local navigation Corridor-following Task Phase 1 Phase 2 Average training “Best” possible Current work: acquiring topological maps based on these primitives