Risk-Sensitive Reinforcement Learning with Smoothed Quantile Objectives
Reinforcement Learning (RL) has achieved tremendous success in recent years. However, the classical foundations of RL do not account for the risk sensitivity of the objective function, which is critical in various fields, including healthcare, finance, etc. A popular approach to incorporate risk sensitivity is to optimize a specific quantile of the cumulative reward distribution. However, exact quantile objectives are non-smooth and can change abruptly under small perturbations of the return distribution, making them difficult to optimize reliably when the transition model must be learned from data. Motivated by this instability, we develop UCB-BQRL, a model-based optimistic learning algo...
| Authors | Mohammad Alipour-Vaezi, Huaiyang Zhong, Sajad Khodadadian |
|---|---|
| Primary category | cs.LG |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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