Project Grant 2515285
Award Date
8/1/25
Completion Date
7/31/28
Dollars Obligated
$155K
Federal Agency
Division of Mathematical Sciences
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Madison, WI 53715, USA
Similar Awards
This Project Grant award, valued at $569,138, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The award supports the development of new methods for actively testing autonomous decision-making systems that utilize reinforcement learning (RL) algorithms. The key objectives are to derive optimal policies for evaluating RL-based autonomous systems, create novel adaptive sampling algorithms to improve policy...
The National Science Foundation (NSF) awarded a $100,000 Project Grant under its Integrative Activities program (CFDA 47.083) to Rutgers, The State University located in Piscataway, New Jersey. The three-year grant, awarded on August 1, 2023, will fund research to develop novel statistical inference tools and computationally efficient approaches for reinforcement learning in high-dimensional, non-identically distributed data settings. Key focus areas include statistical inference for...
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, totaling $236,099, was provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the University of Wisconsin - Madison. The award supports fundamental research focused on developing robust optimization and learning algorithms capable of handling dynamic and uncertain data scenarios, such as distributional shifts between training and test data or data distributions that change in response to the...
This Project Grant award of $175,000 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research to enhance the reliability of data analysis in modern, large-scale datasets, particularly in the era of artificial intelligence. The project develops new statistical and computational methods to address fundamental challenges related to robustness, adaptivity, and structure in modern algorithms and statistical procedures. The research...
This Project Grant award of $356,488 from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) aims to develop a resilient reinforcement learning (RL) framework for managing heterogeneous multi-agent systems in complex and structured environments. The key products and services to be delivered include: Fully decentralized policy optimization algorithms with convergence and complexity analysis for managing cooperative agents with heterogeneous constraints in various structured...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program, totaling $209,267, will fund research to close the simulation-to-reality (sim-to-real) gap in reinforcement learning (RL). The research will develop new techniques using randomization, alignment, and derivation mechanisms to improve the applicability and generalization of RL systems from simulated to real-world environments. The goal is to...
This federal Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) provides $193,000 to the Trustees of Princeton University over a 3-year period starting September 1, 2024. The funding supports collaborative research on developing safe reinforcement learning techniques that can be applied in domains like robotics, autonomous driving, and power systems. The key research thrusts include: 1) training robust policies using distributionally robust approaches;...
This $398,856 National Science Foundation project grant supports research at Northwestern University to improve reinforcement learning algorithms. Specifically, the grant funds the development of sample-efficient and computationally-efficient algorithms for both online and offline reinforcement learning with function approximation. The researchers aim to incorporate optimistic exploration and pessimistic exploitation techniques using faithful uncertainty quantification for neural networks....
This Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) provides $480,225.00 to the University of Illinois to support foundational research on nonlinear filtering and reinforcement learning (RL) algorithms. The goal is to develop new methods for analyzing the stability of nonlinear filters and apply these techniques to create a new class of efficient RL algorithms. The research plan has two main parts: 1) Developing stochastic stability theory...