Risk-Sensitive Reinforcement Learning via Coherent Risk Measures: Framework and Algorithms — NSF Award to University of California
Reinforcement learning (RL) is an area of machine learning where agents learn from interacting with environment to determine actions. RL tools have been widely used in many different engineering systems such as power grid, wireless communications, and autonomous driving etc. A common goal in these decision-making tasks
| Award title | Risk-Sensitive Reinforcement Learning via Coherent Risk Measures: Framework and Algorithms |
|---|---|
| Award ID | 2448268 |
| Awardee | University of California-Davis |
| City | DAVIS |
| State | CA |
| Amount obligated | $360,000 |
| Principal investigator | Lifeng Lai |
| Program | CSCS: Circuits and Systems for |
| Start date | 10/01/2025 |
| Abstract | Reinforcement learning (RL) is an area of machine learning where agents learn from interacting with environment to determine actions. RL tools have been widely used in many different engineering systems such as power grid, wireless communications, and autonomous driving etc. A common goal in these decision-making tasks is to determine an optimal policy that minimizes the expected total discounted cost, which is also named risk-neutral approach. Although the risk-neutral approach is quite popular |
| Source | NSF Awards |
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