1 citations · 1 across the 4 of their papers we have counts for
4 papers
Learning Causally Invariant Reward Functions from Diverse Demonstrations
Ivan Ovinnikov, Eugene Bykovets, Joachim M. Buhmann
Inverse reinforcement learning methods aim to retrieve the reward function of a Markov decision process based on a dataset of expert demonstrations. The commonplace scarcity and he…
How to Enable Uncertainty Estimation in Proximal Policy Optimization
Eugene Bykovets, Yannick Metz, Mennatallah El-Assady +2
While deep reinforcement learning (RL) agents have showcased strong results across many domains, a major concern is their inherent opaqueness and the safety of such systems in real…
BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learning
Eugene Bykovets, Yannick Metz, Mennatallah El-Assady +2
Robustness to adversarial perturbations has been explored in many areas of computer vision. This robustness is particularly relevant in vision-based reinforcement learning, as the…
Inverse Reinforcement Learning via Matching of Optimality Profiles
Luis Haug, Ivan Ovinnikov, Eugene Bykovets
The goal of inverse reinforcement learning (IRL) is to infer a reward function that explains the behavior of an agent performing a task. The assumption that most approaches make is…