4 papers · 1 filter
Counterfactual Residual Data Augmentation for Regression
Hossein Mohebbi, Oliver Schulte, Ke Li +1
Data-driven modeling in real-world regression tasks often suffers from limited training samples, high collection costs, and noisy observations. Inspired by the impact of data augme…
Measures of Variability for Risk-averse Policy Gradient
Yudong Luo, Yangchen Pan, Jiaqi Tan +1
Risk-averse reinforcement learning (RARL) is critical for decision-making under uncertainty, which is especially valuable in high-stake applications. However, most existing works f…
A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges
Guiliang Liu, Sheng Xu, Shicheng Liu +3
Inverse Constrained Reinforcement Learning (ICRL) is the task of inferring the implicit constraints that expert agents adhere to, based on their demonstration data. As an emerging…
Why Online Reinforcement Learning is Causal
Oliver Schulte, Pascal Poupart
Reinforcement learning (RL) and causal modelling naturally complement each other. The goal of causal modelling is to predict the effects of interventions in an environment, while t…