NSF award 2515896 · Washington University

Taming sequential decision-making with reinforcement learning: non-stationarity, heterogeneity, and online/offline comparison

Final report due

29 December 2028

in 803 days · The award ends on 31 August 2028.

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award ending 2028-08-31 · Research.gov
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award ending 2028-08-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Aug 2028in 673 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due29 Dec 2028in 803 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due29 Dec 2028in 803 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out29 Dec 2028in 803 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

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NSF lists this award's end date as 31 August 2028.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 29 December 2028.

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Award card

Award number: 2515896
Period of support: 1 Sep 2025 – 31 Aug 2028
Amount: $149,997
Awardee: Washington University, Saint Louis, MO
Program: OFFICE OF MULTIDISCIPLINARY AC, STATISTICS
Directorate: MPS

Abstract

Many decision-making tasks in healthcare, business, and economics can be naturally framed as online sequential decision-making problems, where decisions are made and outcomes are observed iteratively to achieve long-term objectives. Reinforcement learning (RL) offers a powerful framework and has achieved significant success in engineering domains, including robotics and gaming. However, human-centered tasks — such…

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