This thesis builds on recent work on Unscented Dynamic Programming (UDP)—which eliminates dynamics derivative computations in DDP—to support general nonlinear state and input constraints to high precision using an augmented Lagrangian. It then leverages parallel computations for increased throughput and systematically analyzes the insights, challenges, tradeoffs, and benefits of implementing a parallelized variant of DDP on both a multi-core CPU and a graphics processing unit (GPU).
We build on recent work on Unscented Dynamic Programming (UDP) - which eliminates dynamics derivative computations in DDP - to support general nonlinear state and input constraints using an augmented Lagrangian. The resulting algorithm has the same computational cost as first-order penalty-based DDP variants, but can achieve constraint satisfaction to high precision without the numerical ill-conditioning associated with penalty methods.