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...
This $271,343 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research into developing robust machine learning and inference methods that can withstand data corruption and distribution shifts. The project aims to explore new techniques for structured learning, supervised learning, and reinforcement learning that are resilient to these challenges, with potential applications in healthcare,...
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) 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 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program, with CFDA number 47.049, will fund research to address the challenge of reliable and interpretable reinforcement learning (RL) systems in complex, data-limited environments. The $154,999 award, effective August 1, 2025 through July 31, 2028, aims to develop theoretical foundations and methods for robust inference and decision-making in RL, including tools for contextual bandits...
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 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 $948,000 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support the development of new methods for data-efficient decision-focused learning to address uncertainty in various real-world decision-making problems. The research aims to create a general framework for pre-training key components and rapidly fine-tuning them for specific decision-making tasks, such as in public health,...
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 $439,425 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 grant supports research to enable the safe deployment of learning-enabled systems that can robustly learn and optimize their behavior based on uncertain human feedback and intent. The key objectives are to: (1) develop methods for providing probabilistic performance guarantees when learning policies from human input,...