3 papers
cs.LG2023
Contextual Conservative Q-Learning for Offline Reinforcement Learning
Ke Jiang, Jiayu Yao, Xiaoyang Tan
Offline reinforcement learning learns an effective policy on offline datasets without online interaction, and it attracts persistent research attention due to its potential of prac…
cs.LG2022
Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry
Mark Penrod, Harrison Termotto, Varshini Reddy +3
For responsible decision making in safety-critical settings, machine learning models must effectively detect and process edge-case data. Although existing works show that predictiv…
cs.LG2022
Policy Optimization with Sparse Global Contrastive Explanations
Jiayu Yao, Sonali Parbhoo, Weiwei Pan +1
We develop a Reinforcement Learning (RL) framework for improving an existing behavior policy via sparse, user-interpretable changes. Our goal is to make minimal changes while gaini…