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...
This four-year, $1.2 million project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop new efficient and scalable distributed learning algorithms and frameworks. The grantee, Michigan State University, will systematically investigate computation and communication efficiency challenges in centralized and decentralized machine learning paradigms. Researchers will address these issues through three research directions to...
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 $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....
The National Science Foundation (NSF) awarded a $237,028 Project Grant to New York University (NYU) under the Computer and Information Science and Engineering program (CFDA 47.070) to develop statistical and algorithmic foundations for robust policy learning in uncertain environments. The goal is to create provably efficient techniques for learning optimization policies that can be deployed in practical settings where the training and operational environments differ, such as when using digital...
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 National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program Project Grant (CFDA 47.070) of $151,593 awarded to Cornell University on March 1, 2024 will support the development of new reinforcement learning (RL) algorithms that can learn efficiently and reliably from limited training data. The key products of this 5-year project will be RL algorithms that can be safely deployed in real-world applications like autonomous driving and generative AI where...
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 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 $118,238 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program supports research to advance reinforcement learning (RL) algorithms and frameworks. The principal investigator (PI) at the University of Texas at Austin will develop a unified, principled objective that applies to both standard and offline RL settings. This work aims to enable efficient solutions to large-scale, real-world sequential decision-making...
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 information. Key activities include developing a new theoretical framework; designing robust algorithms resilient to dynamic environments, communication delays, and asynchronous updating; and creating algorithms secure against adversaries introducing untrustworthy information. Concurrently, the team will design and maintain an open-source software framework for empirically validating distributed reinforcement learning methods. The work aims to produce general algorithms applicable where distributed decision making and learning are needed with streaming data and adversaries present.