| NSF Org: |
DMS Division Of Mathematical Sciences |
| Recipient: |
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| Initial Amendment Date: | July 18, 2022 |
| Latest Amendment Date: | September 6, 2022 |
| Award Number: | 2153915 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Tomek Bartoszynski
tbartosz@nsf.gov (703)292-4885 DMS Division Of Mathematical Sciences MPS Directorate for Mathematical and Physical Sciences |
| Start Date: | August 1, 2022 |
| End Date: | July 31, 2026 (Estimated) |
| Total Intended Award Amount: | $655,634.00 |
| Total Awarded Amount to Date: | $655,634.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
2550 NORTHWESTERN AVE # 1100 WEST LAFAYETTE IN US 47906-1332 (765)494-1055 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
Young Hall West Lafayette IN US 47907-2114 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
PROBABILITY, OE Operations Engineering, OFFICE OF MULTIDISCIPLINARY AC |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041, 47.049 |
ABSTRACT
Reinforcement learning (RL) methods have been embraced, in both academic and industrial settings, for solving a range of science and engineering problems involving dynamic system optimization. These problems run the gamut and include optimal resource allocation in, for example, ride-sharing, healthcare management and energy systems, pricing and trading risky assets in finance, autonomous vehicles and robots. RL is also increasingly being used in the physical sciences, for instance for discovering new materials and/or exploring the properties of known materials. Despite the growing importance and breadth of applications, RL methods are often found to perform poorly in actuality. RL methods have been primarily developed for discrete-time and so-called Markovian settings, while most real-world problems are better modeled in continuous time, with non-Markovian dynamics. The broad adoption of RL methods across science and engineering necessitates the investigation of how to develop RL methods for continuous-time and non-Markovian settings. This project aims at laying the foundations for addressing these questions. In addition, the PIs have developed specific aims in terms of dissemination of discoveries, survey for graduate students, national and international networking, mentoring of junior researchers as well as graduate and undergraduate students, participation and organization of events, and interdisciplinary research.
The successful completion of this project will fill make significant contributions towards the theoretical analysis of continuous-time RL. The project will offer a global framework valid for general random environments. In particular it goes beyond the somewhat restrictive Markov setting, and allows for pathwise controls. At its core, this research project aims at the development of analytical results that can be used to provide theoretical guarantees for continuous-time RL problems across a range of application domains. The successful completion of the project will entail a number of new results characterizing the solution of pathwise optimal control in rough environments, the analysis of computational methods for obtaining optimal policies, as well as the analysis of numerical schemes for approximating policies and value functions using rough path signatures. The proposed efforts will have sufficient novelty to open new research areas. They will also further promote the applicability of the theoretical techniques alluded to above.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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