Random sampling of states in dynamic programming.
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.
Atkeson
Christopher G
CG
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Stephens
Benjamin J
BJ
D003198:Computer Simulation; D005246:Feedback; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D011382:Programming, Linear; D012371:Robotics / Q000379:methods*; D013598:Systems Theory