The University of Illinois was awarded a $500,000 Project Grant from the National Science Foundation Division of Information and Intelligent Systems to support research activities related to reinforcement learning in non-stationary environments. Specifically, the grant will fund the development of techniques for safe reinforcement learning with fast adaptation and disturbance prediction capabilities. The work advances the National Science Foundation's Computer and Information Science and...
This three-year, $375,000 National Science Foundation project grant supports research at the University of Illinois to develop distributionally robust adaptive control methodologies for reinforcement learning algorithms. The goal is to enable safe and robust autonomous operation of complex systems by constructing a new class of adaptive controllers that are robust to errors in learned distributions. This will allow reinforcement learning algorithms to directly interact with the controllers...
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;...
This Project Grant award, valued at $569,138, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The award supports the development of new methods for actively testing autonomous decision-making systems that utilize reinforcement learning (RL) algorithms. The key objectives are to derive optimal policies for evaluating RL-based autonomous systems, create novel adaptive sampling algorithms to improve policy...
The National Science Foundation (NSF) awarded a $194,000 Project Grant under the Engineering program (CFDA 47.041) to Georgia Tech Research Corp to conduct research on safe reinforcement learning. The project aims to develop new approaches for training, improving, and evaluating reinforcement learning policies that are robust to distribution shift and non-stationarity, with the goal of ensuring safety in applications such as robotics, autonomous driving, and power systems. Key innovations...
This $250,000 Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) aims to address challenges in stochastic nonlinear control and learning for dynamical systems through a novel "Spectral Dynamic Embedding" approach. Led by the Georgia Tech Research Corporation, the research intends to develop computationally efficient control algorithms suitable for applications in robotics, aerospace, manufacturing, and beyond. The key innovations involve...
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 $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 Project Grant award from the National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems under the NSF Directorate for Engineering (CFDA 47.041) provides $441,162 to the University of Washington to develop innovative variational optimal transport methods for nonlinear filtering algorithms to improve the reliable and safe operation of autonomous systems. The research objectives include computational development and evaluation of the proposed algorithms,...
This Project Grant award, valued at $422,734.00, was provided by the Division of Civil, Mechanical, and Manufacturing Innovation within the National Science Foundation (NSF) Engineering program (CFDA 47.041). The award supports research aimed at developing a theoretical framework to analyze dynamical systems with both fast and slow time-varying signals and parameters, including abrupt switching. The research seeks to combine tools from nonlinear system theory, switched and hybrid systems, and...