10 citations · 10 across the 4 of their papers we have counts for
5 papers
Nonparametric Variance-Penalized Actor-Critic: Statistical Inference for Risk-Sensitive Reinforcement Learning
Saunak Kumar Panda, Tong Li, Yisha Xiang +1
Variance penalization is a principled approach to risk-sensitive reinforcement learning (RL) that explicitly trades expected return for policy stability. Existing methods require a…
Certifying Lower Bounds for Risk-Sensitive Reinforcement Learning under Adversarial State Perturbations
Tong Li, Saunak Kumar Panda, Yisha Xiang
Reinforcement learning (RL) agents deployed in real-world environments are often vulnerable to adversarial perturbations in state observations, creating risks in safety-critical ap…
Dynamic resource matching in manufacturing using deep reinforcement learning
Saunak Kumar Panda, Yisha Xiang, Ruiqi Liu
Matching plays an important role in the logical allocation of resources across a wide range of industries. The benefits of matching have been increasingly recognized in manufacturi…
Online Statistical Inference of Constant Sample-averaged Q-Learning
Saunak Kumar Panda, Tong Li, Ruiqi Liu +1
Reinforcement learning algorithms have been widely used for decision-making tasks in various domains. However, the performance of these algorithms can be impacted by high variance…
Asymptotic Analysis of Sample-averaged Q-learning
Saunak Kumar Panda, Ruiqi Liu, Yisha Xiang
Reinforcement learning (RL) has emerged as a key approach for training agents in complex and uncertain environments. Incorporating statistical inference in RL algorithms is essenti…