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 $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 National Science Foundation awarded a $600,000 Project Grant to Princeton University under the Computer and Information Science and Engineering program (CFDA 47.070). The grant will support research toward developing a mathematical foundation for deep reinforcement learning over the period from October 1, 2022 to September 30, 2026. Specifically, the project aims to bridge current gaps in theoretical reinforcement learning and deep neural networks by investigating guarantees achievable by...
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 (NSF) Division of Mathematical Sciences awarded a $331,902 Project Grant to the Trustees of Boston University on August 15, 2023 under the Mathematical and Physical Sciences program (CFDA 47.049). The purpose of this 3-year grant is to develop rigorous mathematical analysis and theory for the training algorithms used in neural network models across various machine learning applications. The research will leverage stochastic analysis and weak convergence theory...
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...
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 $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 $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 NSF CISE program Project Grant award of $544,114 to New York University (NYU) is funding research on "NUMERICALLY EFFICIENT REINFORCEMENT LEARNING FOR CONSTRAINED SYSTEMS WITH SUPER-LINEAR CONVERGENCE (NERL)". The project aims to develop new reinforcement learning algorithms that can more efficiently create behaviors for real-world robotic applications, while ensuring operational safety. The research will explore ways to improve learning efficacy and guarantee safety, and will...