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 show that we can now solve problems we couldn’t solve previously.

• Type: Conference
• Year: 2007
• Where: NIPS
• Authors: Christopher G. Atkeson, Benjamin Stephens
• Keywords: Associated Value Function, Information Technology, Local Trajectory Optimizers, NIPS 2007, Value Function