Project Grant W911NF261A186

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 $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, 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...

CAREER: Safety-Centered Multi-Agent Reinforcement Learning Wake Forest University received $520,013 in Project Grant funding from the National Science Foundation's Division of Information and Intelligent Systems (CFDA 47.070) awarded on July 15, 2025, with a completion date of June 30, 2030. This CAREER award supports research and development of a comprehensive safety framework for multi-agent reinforcement learning (MARL) systems. The project delivers four key innovations: (1) learning...

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,...

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 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 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...

This Project Grant award from the National Science Foundation (NSF) Office of Advanced Cyberinfrastructure under the CFDA program "Computer and Information Science and Engineering" provides $150,000 to Duke University to conduct collaborative research on developing efficient and provable algorithms for approximate sampling-based exploration strategies in sequential decision-making problems. The key objectives are to unify exploration strategies across different applications like...

Purdue University was awarded a $450,000 project grant from the National Science Foundation's Social, Behavioral, and Economic Sciences program (CFDA 47.075) to advance trustworthy reinforcement learning techniques for online decision making. The three-year award beginning August 15, 2022 will support research into robustness, fairness, causality and explainability in dynamic pricing, dynamic assortment selection and two-sided market matching. The University will develop new theoretical tools,...