skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


This content will become publicly available on June 9, 2025

Title: Achieving the Asymptotically Minimax Optimal Sample Complexity of Offline Reinforcement Learning: A DRO-Based Approach
Award ID(s):
2229873
PAR ID:
10573577
Author(s) / Creator(s):
; ;
Publisher / Repository:
Open Journal
Date Published:
Journal Name:
Transactions on Machine Learning Research
ISSN:
2835-8856
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
No document suggestions found
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email