21 citations · 33 across the 12 of their papers we have counts for
12 papers
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…
Adversarial Imitation Learning from Visual Observations using Latent Information
Vittorio Giammarino, James Queeney, Ioannis Ch. Paschalidis
We focus on the problem of imitation learning from visual observations, where the learning agent has access to videos of experts as its sole learning source. The challenges of this…
Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees
James Queeney, Erhan Can Ozcan, Ioannis Ch. Paschalidis +1
Robustness and safety are critical for the trustworthy deployment of deep reinforcement learning. Real-world decision making applications require algorithms that can guarantee robu…
Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning
James Queeney, Mouhacine Benosman
Many real-world domains require safe decision making in uncertain environments. In this work, we introduce a deep reinforcement learning framework for approaching this important pr…
Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation
Vittorio Giammarino, James Queeney, Lucas C. Carstensen +2
We investigate the use of animal videos (observations) to improve Reinforcement Learning (RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by theor…
Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse
James Queeney, Ioannis Ch. Paschalidis, Christos G. Cassandras
We develop a new class of model-free deep reinforcement learning algorithms for data-driven, learning-based control. Our Generalized Policy Improvement algorithms combine the polic…
Generalized Proximal Policy Optimization with Sample Reuse
James Queeney, Ioannis Ch. Paschalidis, Christos G. Cassandras
In real-world decision making tasks, it is critical for data-driven reinforcement learning methods to be both stable and sample efficient. On-policy methods typically generate reli…
Uncertainty-Aware Policy Optimization: A Robust, Adaptive Trust Region Approach
James Queeney, Ioannis Ch. Paschalidis, Christos G. Cassandras
In order for reinforcement learning techniques to be useful in real-world decision making processes, they must be able to produce robust performance from limited data. Deep policy…