NSF award 2448268 · University of California-Davis

Risk-Sensitive Reinforcement Learning via Coherent Risk Measures: Framework and Algorithms

Final report due

28 January 2029

in 833 days · The award ends on 30 September 2028.

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PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice20 Sep 2028in 703 days
award ending 2028-09-30 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Jan 2029in 833 days
award ending 2028-09-30 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Jan 2029in 833 days
award ending 2028-09-30 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Jan 2029in 833 days
award ending 2028-09-30 · 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 30 September 2028.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 January 2029.

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Award card

Award number: 2448268
Period of support: 1 Oct 2025 – 30 Sep 2028
Amount: $360,000
Awardee: University of California-Davis, Davis, CA
Program: CSCS: Circuits and Systems for
Directorate: ENG

Abstract

Reinforcement learning (RL) is an area of machine learning where agents learn from interacting with environment to determine actions. RL tools have been widely used in many different engineering systems such as power grid, wireless communications, and autonomous driving etc. A common goal in these decision-making tasks is to determine an optimal policy that minimizes the expected total discounted cost, which is also…

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