NSF award 2533864 · Texas A&M Engineering Experiment Station

Collaborative Research: Decentralized Multi-Agent Reinforcement Learning (MARL) Algorithms for Solving Constrained Markov Games

Final report due

28 October 2029

in 1,106 days · The award ends on 30 June 2029.

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award ending 2029-06-30 · Research.gov
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Extension request16 May 2029in 941 days
award ending 2029-06-30 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice20 Jun 2029in 976 days
award ending 2029-06-30 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Oct 2029in 1106 days
award ending 2029-06-30 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Oct 2029in 1106 days
award ending 2029-06-30 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Oct 2029in 1106 days
award ending 2029-06-30 · 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 June 2029.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 October 2029.

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Award card

Award number: 2533864
Period of support: 1 Jul 2026 – 30 Jun 2029
Amount: $300,000
Awardee: Texas A&M Engineering Experiment Station, College Station, TX
Program: EPCL: Energy, Power, Control,
Directorate: ENG

Abstract

This project seeks to develop new methods that allow multiple autonomous decision-makers, such as robots, energy resources, or actuators, to learn how to operate safely and efficiently in dynamic and uncertain environments without relying on a central coordinator. In many emerging technologies, individual components must make decisions based only on limited local information, while collectively satisfying safety,…

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