  This paper presents a sampling-based method for optimal motion planning in
non-holonomic systems in the absence of known cost functions. It uses the
principle of learning through experience to deduce the cost-to-go of regions
within the workspace. This cost information is used to bias an incremental
graph-based search algorithm that produces solution trajectories. Iterative
improvement of cost information and search biasing produces solutions that are
proven to be asymptotically optimal. The proposed framework builds on
incremental Rapidly-exploring Random Trees (RRT) for random sampling-based
search and Reinforcement Learning (RL) to learn workspace costs. A series of
experiments were performed to evaluate and demonstrate the performance of the
proposed method.
