Using Local Trajectory Optimizers to Speed up Global Optimization in Dynamic Programming
Abstract
Dynamic programming provides a methodology to develop planners and controllers for nonlinear systems. However, general dynamic programming is computationally intractable. We have developed procedures that allow more complex planning and control problems to be solved. We use second order local trajectory optimization to generate locally optimal plans and local models of the value function and its derivatives. We maintain global consistency of the local models of the value function, guaranteeing that our locally optimal plans are actually globally optimal, up to the resolution of our search procedures.
Cite
Text
Atkeson. "Using Local Trajectory Optimizers to Speed up Global Optimization in Dynamic Programming." Neural Information Processing Systems, 1993.Markdown
[Atkeson. "Using Local Trajectory Optimizers to Speed up Global Optimization in Dynamic Programming." Neural Information Processing Systems, 1993.](https://mlanthology.org/neurips/1993/atkeson1993neurips-using/)BibTeX
@inproceedings{atkeson1993neurips-using,
title = {{Using Local Trajectory Optimizers to Speed up Global Optimization in Dynamic Programming}},
author = {Atkeson, Christopher G.},
booktitle = {Neural Information Processing Systems},
year = {1993},
pages = {663-670},
url = {https://mlanthology.org/neurips/1993/atkeson1993neurips-using/}
}