Receding horizon differential dynamic programming

The control of high-dimensional, continuous, non-linear dynamical systems is a key problem in reinforcement learning and control. Local, trajectory-based meth-ods, using techniques such as Differential Dynamic Programming (DDP), are not directly subject to the curse of dimensionality, but generate only local controllers. In this paper,we introduce Receding Horizon DDP (RH-DDP), an extension to the classic DDP algorithm, which allows us to construct stable and robust controllers based on a library of local-control trajectories. We demonstrate the effective-ness of our approach on a series of high-dimensional problems using a simulated multi-link swimming robot. These experiments show that our approach effectively circumvents dimensionality issues, and is capable of dealing with problems of (at least) 24 state and 9 action dimensions.