Project Grant 2515896
- This federal Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program aims to develop tools and methods to improve decision-making and enhance interpretability in reinforcement learning (RL) algorithms operating in complex, data-limited environments. The $154,999 award supports research to address challenges in ensuring RL systems are statistically robust, interpretable, and socially responsible for real-world...
- 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...
- The University of Washington was awarded a three-year $500,000 Project Grant from the National Science Foundation Division of Information and Intelligent Systems under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The grant will support research into the theoretical foundations of reinforcement learning, from modeling learning as a blank slate to using function approximation. The University will conduct investigator-initiated research advancing the...
- 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 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 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 $249,987 Project Grant awarded by the National Science Foundation (CFDA 47.070 Computer and Information Science and Engineering) supports a collaborative research initiative to develop a mathematical foundation for deep reinforcement learning (DRL). The key objectives are to characterize the computational and statistical complexity of DRL, and to design more efficient and reliable methods for DRL applications in real-world domains such as robotics, healthcare, and transportation systems....
- 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...
- The University of Illinois was awarded a $500,000 Project Grant from the National Science Foundation Division of Information and Intelligent Systems to support research activities related to reinforcement learning in non-stationary environments. Specifically, the grant will fund the development of techniques for safe reinforcement learning with fast adaptation and disturbance prediction capabilities. The work advances the National Science Foundation's Computer and Information Science and...
- 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 U.S. National Science Foundation (NSF) awarded a 3-year, $149,997 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program to The Washington University in University City, Missouri. The grant funding supports research to address key challenges in applying reinforcement learning (RL) techniques to complex, high-stakes decision-making tasks in domains such as healthcare, business, and economics. The project aims to:
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $150.0k | 8/14/25 |