3 citations · 6 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging task
Vittorio Giammarino, Matthew F Dunne, Kylie N Moore +3
We develop a simple framework to learn bio-inspired foraging policies using human data. We conduct an experiment where humans are virtually immersed in an open field foraging envir…
Online Baum-Welch algorithm for Hierarchical Imitation Learning
Vittorio Giammarino, Ioannis Ch. Paschalidis
The options framework for hierarchical reinforcement learning has increased its popularity in recent years and has made improvements in tackling the scalability problem in reinforc…