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 $280,800 federal Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The award, titled "CAREER: DIFFERENTIABLE EVOLUTION: EFFICIENT AUTOMATIC DESIGN OF EMBODIED INTELLIGENCE", will support research by Northwestern University to explore efficient automatic optimization techniques for the design of robots. The goal is to develop a "differentiable" representation of...
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;...
Northwestern University was awarded a $597,870 Project Grant from the National Science Foundation Division of Undergraduate Education to support the project "CAREER: TOWARDS INTELLIGENT LEARNING ENVIRONMENTS THAT SUPPORT THE PRACTICE OF PROGRAMMING" from June 1, 2021 through May 31, 2026. This award will fund research and education activities under the NSF's Computer and Information Science and Engineering program (CFDA 47.070), which supports investigator-initiated research and...
This National Science Foundation (NSF) CAREER grant under the Engineering program (CFDA 47.041) provides $517,612 in funding to the University of Texas at Austin. The grant supports research to develop new foundations for scalable and resilient distributed reinforcement learning in open multi-agent systems. The overarching goal is to design learning and control methods that enable autonomous agents to interact effectively, adapt to time-varying environments, and be resilient to failures and...
The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded a $500,000 Project Grant to Texas A&M Engineering Experiment Station, doing business as Tees, to support research towards a principled framework for resilient, data efficient and scalable reinforcement learning for control. The award period is from February 1, 2021 through January 31, 2026. The research is funded under the NSF Directorate for Engineering's Engineering program (CFDA 47.041), which...
This $500,000 Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) aims to advance trustworthy artificial intelligence and machine learning (AI/ML), particularly in reinforcement learning (RL) systems used in critical applications like healthcare, education, and commerce. The research focuses on four key thrusts: (1) developing theoretical results for private RL, (2) investigating the interplay between robustness and privacy, (3) creating a framework...
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...
The National Science Foundation (NSF) awarded a $425,225 CAREER grant under the Engineering program (CFDA 47.041) to the University of Maryland, College Park. The grant, titled "CAREER: Foundations of Dynamic Multi-Agent Learning Under Information Constraints," aims to develop the theoretical foundations for multi-agent learning in dynamic environments with information constraints. The research program will span three main thrusts: (1) formally introducing information structure...
This National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems award, CFDA 47.041 Engineering, will provide $193,000 from September 1, 2024 to August 31, 2027 to New York University (NYU) to develop new theories and methodologies for safe reinforcement learning in domains such as robotics, autonomous driving, and power systems. The key products and services to be delivered under this Project Grant include: 1) Formulating safety measures as general objectives...
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 through innovative engineering research and education, this award aims to advance principled approaches to deep reinforcement learning that can help solve challenges facing communities. Northwestern University will leverage its expertise in this area to design and evaluate new deep learning methods under this project grant.