Project Grant 2530001
Award Date 1/1/26
Completion Date 12/31/28
Dollars Obligated $300K
Federal Agency
Federal Grant Program
Assistance Type
Project Grant
Place of Performance
Hattiesburg, MS 39406, USA
Similar Awards
- This $299,999 Project Grant awarded by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to enhance the performance of reinforcement learning (RL) systems in completing complex tasks in challenging environments. The project aims to develop new task and environment representations to enable active learning strategies that optimize resource allocation and reduce the need for extensive physical interactions with the...
- This Project Grant award of $398,990.00 from the National Science Foundation's Computer and Information Science and Engineering (CISE) program, awarded on August 1, 2025, supports research by the University of California, Irvine (UCI) to develop theoretical foundations for multi-agent learning systems. The project aims to design computationally efficient algorithms that can reliably converge to equilibrium states in cooperative and competitive multi-agent environments, as well as address open...
- This Project Grant award of $299,631 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support research at Purdue University focused on integrating foundational AI models into reinforcement learning (RL) algorithms. The goal is to develop new theoretical frameworks and methods that allow RL systems to acquire new skills more quickly, perform better in unfamiliar situations, and be deployed more rapidly in real-world...
- This $749,963 Project Grant was awarded by the National Science Foundation (NSF) Division of Computing and Communication Foundations on October 1, 2023. The grant is funded under the NSF's Computer and Information Science and Engineering (CFDA #47.070) program, which supports investigator-initiated research and education across all areas of computing, communications, and information science and engineering. The grant was awarded to Northeastern University, a private, non-profit research...
- This National Science Foundation (NSF) project grant under the Computer and Information Science and Engineering (CISE, CFDA 47.070) program addresses critical challenges in applying reinforcement learning (RL) to real-world urban environments. The $353,369 project, awarded to Arizona State University (UEI: NTLHJXM55KZ6), aims to develop actionable data analytics tailored to urban decision-making, focusing on issues like noisy/incomplete observations, complex system behaviors, and the need for...
- This $750,000 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program to Arizona State University focuses on developing foundational technologies for safe Reinforcement Learning (RL)-enabled systems. The 4-year project aims to establish theories, algorithms, and experiments for distributional RL to enable policy safety, exploration safety, and environmental safety in RL-powered applications like 6G networking,...
- This Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) provides $356,488 to the Texas A&M Engineering Experiment Station (Tees) to develop a resilient reinforcement learning (RL) framework for managing heterogeneous multi-agent systems in complex and structured environments. The research aims to produce scalable and computationally-efficient RL algorithms with rigorous convergence and complexity analysis for applications like interference management...
- This $1,500,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) supports the University of Southern California's research on safe multi-agent systems using a neurosymbolic approach. The project aims to develop new theories and algorithms for the design of safe learning-enabled multi-agent systems, with applications in areas like wildfire prevention using drone swarms and semi-automated...
- This $600,000 National Science Foundation project grant supports research at Stony Brook University to develop a suite of novel distributed reinforcement learning algorithms. The grant is funded through the NSF's Computer and Information Science and Engineering program. Specifically, the three-year award will fund research to establish theoretical foundations for designing, analyzing, and applying fully distributed reinforcement learning algorithms over large-scale networks without global...
- This $300,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to develop trustworthy and resilient coordination mechanisms for multi-agent systems (MAS). The research will advance foundational knowledge in three key areas: (1) detecting and mitigating abnormal behaviors in cooperating agents, (2) managing adversarial or non-cooperative agents using game-theoretic and adversarial machine learning methods, and...