The National Science Foundation (NSF) awarded a $375,000 Project Grant under the Computer and Information Science and Engineering (CISE) program to the Regents of the University of Michigan, Office of Research and Sponsored Projects, doing business as the University of Michigan. The grant, awarded on October 1, 2023, aims to develop foundational technologies for safe Reinforcement Learning (RL)-enabled systems, integrating research and education. The project focuses on three key thrusts: (1)...
This Project Grant award, valued at $375,000 and awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports The Ohio State University's research on safe and reliable reinforcement learning (RL) systems. The key objectives of this 4-year project are to develop foundational technologies for safe RL-enabled systems, including policy safety, exploration safety, and environmental safety. The research will...
This $375,000 federal Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The goal of the project is to develop tools and methods to help ensure the safe operation of autonomous systems that utilize reinforcement learning (RL) algorithms. Key activities include: 1) developing inverse RL algorithms to learn an agent's reward function from demonstrations, 2) exploring the agent's norms to...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program, totaling $209,267, will fund research to close the simulation-to-reality (sim-to-real) gap in reinforcement learning (RL). The research will develop new techniques using randomization, alignment, and derivation mechanisms to improve the applicability and generalization of RL systems from simulated to real-world environments. The goal is to...
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...
The National Science Foundation (NSF) awarded a $750,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to Oregon State University (OSU). The purpose of this 4-year award, with a start date of September 1, 2024, is to develop tools and methods to design provably safe autonomous systems, with a focus on addressing safety challenges in reinforcement learning (RL) agents. Key activities include developing inverse RL algorithms to align agent norms...
This National Science Foundation (NSF) Engineering Program (CFDA 47.041) Project Grant award to Arizona State University (ASU) will develop fundamental algorithms and theoretical limits for distributionally robust reinforcement learning (RL) under model uncertainty. The 5-year, $417,239 award will advance the state-of-the-art in RL by delivering provably convergent, efficient, and minimax optimal robust RL algorithms. The research will have significant impact on sequential decision-making...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) award of $300,000 to Arizona State University (ASU) from August 2024 to July 2027 aims to enhance the performance of reinforcement learning (RL) systems in completing difficult tasks in complex environments. The project seeks to develop task and environment representations specifically for active design in RL, including: 1) Active Environment Design for RL to...
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 $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...