Project Grant 2315725

Award Date 6/15/23
Completion Date 5/31/26
Dollars Obligated $484K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Albany, CA 94710, USA
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The Regents of the University of California, doing business as University of California, Berkeley, received a $483,663 Project Grant award from the National Science Foundation Division of Computing and Communication Foundations under the Computer and Information Science and Engineering federal grant program (CFDA 47.070).

The award will support research from June 15, 2023 through May 31, 2026 to develop theoretically grounded algorithms and analyze sample complexity for partially observable reinforcement learning systems. The research will focus on designing efficient learning algorithms and proving statistical complexity bounds for partially observable Markov decision processes and extensive-form games. The work will build on machine learning, information theory, control theory, and high-dimensional statistics to provide a theoretical foundation for future research in this area. The algorithms will be implemented and tested in an open-source reinforcement learning environment.

As the parent organization, the State of California Controllers Office would administer any subaward funding provided to UC Berkeley to support this research effort. The NSF award is consistent with the agency's mission to support merit-based investigator-initiated computing and communications research.

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