Scalable planning with Tensorflow for hybrid nonlinear domains

Ga Wu, Buser Say, Scott Sanner

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

16 Citations (Scopus)

Abstract

Given recent deep learning results that demonstrate the ability to effectively optimize high-dimensional non-convex functions with gradient descent optimization on GPUs, we ask in this paper whether symbolic gradient optimization tools such as Tensorflow can be effective for planning in hybrid (mixed discrete and continuous) nonlinear domains with high dimensional state and action spaces? To this end, we demonstrate that hybrid planning with Tensorflow and RMSProp gradient descent is competitive with mixed integer linear program (MILP) based optimization on piecewise linear planning domains (where we can compute optimal solutions) and substantially outperforms state-of-the-art interior point methods for nonlinear planning domains. Furthermore, we remark that Tensorflow is highly scalable, converging to a strong plan on a large-scale concurrent domain with a total of 576,000 continuous action parameters distributed over a horizon of 96 time steps and 100 parallel instances in only 4 minutes. We provide a number of insights that clarify such strong performance including observations that despite long horizons, RMSProp avoids both the vanishing and exploding gradient problems. Together these results suggest a new frontier for highly scalable planning in nonlinear hybrid domains by leveraging GPUs and the power of recent advances in gradient descent with highly optimized toolkits like Tensorflow.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 30 (NIPS 2017)
EditorsI. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett
Place of PublicationSan Diego CA USA
PublisherNeural Information Processing Systems (NIPS)
Pages6273-6283
Number of pages11
Volume30
Publication statusPublished - 2017
Externally publishedYes
EventAdvances in Neural Information Processing Systems 2017 - Long Beach, United States of America
Duration: 4 Dec 20179 Dec 2017
Conference number: 30th
https://dl.acm.org/doi/proceedings/10.5555/3295222 (Proceedings)

Publication series

NameAdvances in Neural Information Processing Systems
PublisherNeural Information Processing Systems (NIPS)
ISSN (Print)1049-5258

Conference

ConferenceAdvances in Neural Information Processing Systems 2017
Abbreviated titleNIPS 2017
Country/TerritoryUnited States of America
CityLong Beach
Period4/12/179/12/17
Internet address

Cite this