Taming sequential decision-making with reinforcement learning: non-stationarity, heterogen — NSF Award to Washington University (M
Many decision-making tasks in healthcare, business, and economics can be naturally framed as online sequential decision-making problems, where decisions are made and outcomes are observed iteratively to achieve long-term objectives. Reinforcement learning (RL) offers a powerful framework and has achieved significant su
| Award title | Taming sequential decision-making with reinforcement learning: non-stationarity, heterogen |
|---|---|
| Award ID | 2515896 |
| Awardee | Washington University |
| City | SAINT LOUIS |
| State | MO |
| Amount obligated | $149,997 |
| Principal investigator | Ran Chen |
| Program | OFFICE OF MULTIDISCIPLINARY AC, STATISTICS |
| Start date | 09/01/2025 |
| Abstract | Many decision-making tasks in healthcare, business, and economics can be naturally framed as online sequential decision-making problems, where decisions are made and outcomes are observed iteratively to achieve long-term objectives. Reinforcement learning (RL) offers a powerful framework and has achieved significant success in engineering domains, including robotics and gaming. However, human-centered tasks — such as those in healthcare and business — pose substantial new challenges for RL. Thes |
| Source | NSF Awards |
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