Project Grant 2212261
- This $600,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program will support research at Stanford University toward developing a mathematical foundation for deep reinforcement learning. Over four years, the grant will fund three research thrusts investigating the types of guarantees achievable by reinforcement learning policies under different problem structures and increasing neural network complexity. The researchers will also...
- 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....
- Federal Grant Award Summary The National Science Foundation (NSF) Computer and Information Science and Engineering program (CFDA 47.070) awarded $249,987 to the University of California, Berkeley on July 1, 2025, to conduct collaborative research on building a mathematical foundation for deep reinforcement learning (DRL). This project addresses a critical gap in theoretical understanding of DRL systems, which have achieved significant real-world breakthroughs in robotics, gaming, healthcare, and...
- This five-year project grant from the National Science Foundation's Engineering program (CFDA 47.041) provides $500,000 to Northwestern University for research titled "CAREER: PRINCIPLED DEEP REINFORCEMENT LEARNING FOR SOCIETAL SYSTEMS" from February 2021 through January 2026. The funding supports the development of deep reinforcement learning techniques to address complex problems impacting society. As the Engineering program seeks to improve quality of life and economic strength...
- The National Science Foundation (NSF) awarded a $375,000 Project Grant under the Computer and Information Science and Engineering (CISE) program to the Regents of the University of Michigan, Office of Research and Sponsored Projects, doing business as the University of Michigan. The grant, awarded on October 1, 2023, aims to develop foundational technologies for safe Reinforcement Learning (RL)-enabled systems, integrating research and education. The project focuses on three key thrusts: (1)...
- 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 $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 $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 National Science Foundation Division of Computing and Communication Foundations awarded $800,000 under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to the University of California, Berkeley for a four-year collaborative research project grant. The project aims to improve the sample efficiency of reinforcement learning algorithms in both offline and online settings through techniques like optimistic exploration and pessimistic exploitation. It...
This $1.2 million project grant from the National Science Foundation's Computer and Information Science and Engineering program will support research and education activities at the University of Washington from October 2022 to September 2026. The project aims to develop a mathematical foundation for deep reinforcement learning by investigating guarantees achievable by neural network policies under different problem structures. Researchers will leverage tools from approximation theory, control theory, and optimization to systematically characterize the computational and statistical complexity of deep reinforcement learning. They will also design more efficient and reliable empirical methods. As part of this work, the grantee will mentor students, develop new courses and materials, and organize workshops to advance both research and workforce development in this area. Broader impacts include curriculum development for high school data science and artificial intelligence. Findings could help advance real-world applications of deep reinforcement learning in robotics, gaming, healthcare, and transportation systems.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $600.0k | 6/29/22 |