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 problems, such as in applications like self-driving cars and robotics. The PI will explore connections between imitation learning and RL methods under this dual framework, and investigate approaches for incorporating pre-training and fine-tuning to improve sample efficiency, including leveraging out-of-domain datasets. The award period is from September 1, 2024 to August 31, 2029. No subawards are planned under this grant.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $118.2k | 2/26/24 |