Random Sampling of States in Dynamic Programming
Chris Atkeson, Benjamin J. Stephens · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · 2008
We combine three threads of research on approximate dynamic programming: sparse random sampling of states, value function and policy approximation using local models, and using local trajectory optimizers to globally optimize a policy and associated value function. Our focus is on finding steady-state policies for deterministic time-invariant discrete time control problems with continuous states and actions often found in robotics. In this paper, we describe our approach and provide initial results on several simulated robotics problems.