Synthetic Simulated Environments

Sergey I. Nikolenko ()
Additional contact information
Sergey I. Nikolenko: Synthesis AI

Chapter Chapter 7 in Synthetic Data for Deep Learning, 2021, pp 195-215 from Springer

Abstract: Abstract In this chapter, we proceed from datasets of static synthetic images, either prerendered or procedurally generated, to entire simulated environments that can be used either to generate synthetic datasets on the fly or provide learning environments for reinforcement learning agents. We discuss datasets and simulations for outdoor environments (mostly for autonomous driving), indoor environments, and physics-based simulations for robotics. We also make a special case study of datasets for unmanned aerial vehicles and the use of computer games as simulated environments.

Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-75178-4_7

Ordering information: This item can be ordered from
http://www.springer.com/9783030751784

DOI: 10.1007/978-3-030-75178-4_7

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().