EAGER: Theoretical Foundations for Integrating Foundational Models into Reinforcement Lear — NSF Award to Purdue University (IN, $
Reinforcement learning (RL) is a promising approach for enabling machines, such as robots or cars, to make decisions in complex and unpredictable environments. Examples of these are robots that can run or autonomous cars that can navigate cluttered streets. To make these algorithms work, people use simulation. The prob
| Award title | EAGER: Theoretical Foundations for Integrating Foundational Models into Reinforcement Lear |
|---|---|
| Award ID | 2521982 |
| Awardee | Purdue University |
| City | WEST LAFAYETTE |
| State | IN |
| Amount obligated | $299,631 |
| Principal investigator | Juan Wachs |
| Program | Robust Intelligence |
| Start date | 08/01/2025 |
| Abstract | Reinforcement learning (RL) is a promising approach for enabling machines, such as robots or cars, to make decisions in complex and unpredictable environments. Examples of these are robots that can run or autonomous cars that can navigate cluttered streets. To make these algorithms work, people use simulation. The problem is that in practice, these robots struggle to solve similar challenges in the real-world, due to the lack of controllability in these applications. The more realistic the envir |
| Source | NSF Awards |
$799/mo
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