21 citations · 33 across the 12 of their papers we have counts for
5 papers · 1 filter
GRAM: Generalization in Deep RL with a Robust Adaptation Module
James Queeney, Xiaoyi Cai, Alexander Schperberg +3
The reliable deployment of deep reinforcement learning in real-world settings requires the ability to generalize across a variety of conditions, including both in-distribution scen…
PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain
Xiaoyi Cai, James Queeney, Tong Xu +8
Self-supervised learning is a powerful approach for developing traversability models for off-road navigation, but these models often struggle with inputs unseen during training. Ex…
Visually Robust Adversarial Imitation Learning from Videos with Contrastive Learning
Vittorio Giammarino, James Queeney, Ioannis Ch. Paschalidis
We propose C-LAIfO, a computationally efficient algorithm designed for imitation learning from videos in the presence of visual mismatch between agent and expert domains. We analyz…
Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees
Yilei Chen, Vittorio Giammarino, James Queeney +1
Adversarial Imitation Learning (AIL) faces challenges with sample inefficiency because of its reliance on sufficient on-policy data to evaluate the performance of the current polic…
A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations
Erhan Can Ozcan, Vittorio Giammarino, James Queeney +1
This paper investigates how to incorporate expert observations (without explicit information on expert actions) into a deep reinforcement learning setting to improve sample efficie…