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),...
This $356,488 National Science Foundation (NSF) Engineering program (CFDA 47.041) Project Grant award to Texas A&M Engineering Experiment Station (Tees) aims to develop a resilient reinforcement learning (RL) framework for managing heterogeneous multi-agent systems in complex and structured environments. The overarching objectives are to:
Leverage environment model structures to design fully decentralized policy optimization algorithms with rigorous convergence and complexity analysis...
This Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) provides $425,225 to the University of Maryland, College Park to conduct research on the foundations of dynamic multi-agent learning under information constraints. The key research thrusts of this 5-year award include: 1) formally introducing the concept of "information structure" from decentralized stochastic control into the theoretical studies of dynamic multi-agent learning, 2)...
This $419,577 National Science Foundation (NSF) Engineering program (CFDA 47.041) Project Grant awarded to the Regents of the University of Michigan will fund research to develop scalable and reliable coordination capabilities for embodied intelligent networks. These networks consist of distributed autonomous agents that can sense, reason, communicate, and act, leveraging commands from human operators and/or machine learning algorithms. The research aims to enable these agent networks to...
This National Science Foundation (NSF) Engineering Program (CFDA 47.041) Project Grant award to New York University (NYU) provides $193,000.00 in funding from Sep 1, 2024 to Aug 31, 2027 to develop new theories and methodologies for safe reinforcement learning. The research aims to create policies that are robust to distribution shift, continually improve with Bayesian risk-averse learning, adapt to non-stationary environments via online change detection, and rigorously evaluate policies...
This $118,238 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program supports research to advance reinforcement learning (RL) algorithms and frameworks. The principal investigator (PI) at the University of Texas at Austin will develop a unified, principled objective that applies to both standard and offline RL settings. This work aims to enable efficient solutions to large-scale, real-world sequential decision-making...
This $600,000 National Science Foundation project grant supports research at Stony Brook University to develop a suite of novel distributed reinforcement learning algorithms. The grant is funded through the NSF's Computer and Information Science and Engineering program.
Specifically, the three-year award will fund research to establish theoretical foundations for designing, analyzing, and applying fully distributed reinforcement learning algorithms over large-scale networks without global...
This federal Project Grant award for $300,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of machine learning models to study dynamic decision-making behavior. The project aims to create structural frameworks for understanding how agents, whether human or artificial, make decisions over time in changing environments. This includes developing methodologies for learning structural models of...
This Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports collaborative research to design provably safe autonomous systems. The $375,000 award to the University of Texas at Austin aims to develop tools that can align the norms and behaviors of reinforcement learning (RL) agents with the intent of their designers. Key activities include developing inverse RL algorithms to learn agent reward functions,...
This $357,000 National Science Foundation (NSF) Engineering Program (CFDA 47.041) award to the University of Texas at Austin supports research, education, and outreach activities to develop new methods for understanding and forecasting the behavior of complex biological systems. The project aims to create an analytical framework that merges artificial intelligence with nonlinear dynamics theory to model biological time series data and identify recurring patterns associated with common...