Boosted Distributional Reinforcement Learning: Analysis and Healthcare Applications
Researchers and practitioners are increasingly considering reinforcement learning to optimize decisions in complex domains like robotics and healthcare. To date, these efforts have largely utilized expectation-based learning. However, relying on expectation-focused objectives may be insufficient for making consistent decisions in highly uncertain situations involving multiple heterogeneous groups. While distributional reinforcement learning algorithms have been introduced to model the full distributions of outcomes, they can yield large discrepancies in realized benefits among comparable agents. This challenge is particularly acute in healthcare settings, where physicians (controllers) mu...
| Authors | Zequn Chen, Wesley J. Marrero |
|---|---|
| Primary category | cs.LG |
| Published | 6 April 2026 |
| Last updated at source | 10 April 2026 |
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