Project Grant 2552047
- The National Science Foundation Division of Computing and Communication Foundations awarded Massachusetts Institute of Technology $500,000 on August 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) for collaborative research on reinforcement learning methods for imperfect-information games. The project develops theoretically sound, scalable policy-gradient algorithms and decision-time planning methods that enable deep reinforcement learning to operate...
- 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 National Science Foundation project grant of $146,801 will support research into new approaches for modeling games of incomplete information from July 2022 through June 2025. Funded under the Social, Behavioral, and Economic Sciences program, the research aims to develop robust predictive tools for strategic situations where outcomes depend on unobserved states and players receive private signals. Specifically, the grant will further generalized solution concepts that characterize...
- The National Science Foundation Division of Computing and Communication Foundations awarded the University of Southern California $871,225 on August 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop theoretical foundations for multi-agent learning systems in artificial intelligence. The project advances the theory of online optimization and learning within multi-agent systems through three research thrusts. The first thrust characterizes optimal...
- This National Science Foundation (NSF) Project Grant, awarded under the Computer and Information Science and Engineering program (CFDA 47.070), will fund a collaborative research project to study human planning and decision-making through the analysis of a massive dataset of chess games. The $562,614 award to New York University, with a project period from September 1, 2023 to August 31, 2026, will leverage artificial intelligence techniques to gain insights into how individuals form complex...
- 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...
- The National Science Foundation Division of Computing and Communication Foundations awarded Duke University $420,000 on October 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop theoretical and algorithmic frameworks for off-dynamics reinforcement learning—methods that enable intelligent systems to learn in simulated or indirect environments and transfer that knowledge reliably to real-world deployment scenarios with different transition...
- This Project Grant award of $160,673 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research to combine algorithms and machine learning to improve decision-making under uncertainty. The project, led by New York University (NYU), will explore incorporating machine-learned predictions into algorithm design as well as developing learning models optimized for specific algorithmic objectives. This work aims to create a...
- The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded the University of California, Berkeley $150,000 on September 1, 2026, under the Engineering program (CFDA 47.041) to develop mathematical foundations for reinforcement learning and generative artificial intelligence systems. The project will establish stochastic analysis, stochastic control, and optimization theory for three interconnected research directions: continuous-time reinforcement learning...
- The National Science Foundation Division of Computing and Communication Foundations awarded Syracuse University $371,641 on July 15, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop approximate causal reinforcement learning methods for artificial intelligence decision-making systems. The project addresses two fundamental gaps that limit AI reliability in real-world deployment. First, historical data used to train decision-making systems are shaped...
The National Science Foundation Division of Computing and Communication Foundations awarded New York University $500,000 on August 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop theoretically sound and scalable deep reinforcement learning methods for imperfect-information games—settings where decision makers must act without full knowledge of others' information states. The project delivers three primary components: sound policy-gradient algorithms applicable to imperfect-information games; decision-time planning methods that reason about hidden information without enumerating all possible hidden states; and open-source benchmarks and software for reliable evaluation. The recipient will publicly disseminate resulting algorithms, code, and educational materials to support future research and train students in building reliable AI systems for strategic decision making under uncertainty and imperfect information. Performance occurs at New York University in New York, New York, with a period of performance from August 1, 2026, through July 31, 2030. This is a project grant, a form of assistance funding.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $500.0k | 7/24/26 |