Search Results for author:
Found 4 papers, 0 papers with code
Quantum Imitation Learning
Despite remarkable successes in solving various complex decision-making tasks, training an imitation learning (IL) algorithm with deep neural networks (DNNs) suffers from the high computation burden.
Off-policy Imitation Learning from Visual Inputs
We propose OPIfVI (Off-Policy Imitation from Visual Inputs), which is composed of an off-policy learning manner, data augmentation, and encoder techniques, to tackle the mentioned challenges, respectively.
Lagrangian Generative Adversarial Imitation Learning with Safety
Imitation Learning (IL) merely concentrates on reproducing expert behaviors and could take dangerous actions, which is unbearable in safety-critical scenarios.
On the Guaranteed Almost Equivalence between Imitation Learning from Observation and Demonstration
By contrast, this paper proves that LfO is almost equivalent to LfD in the deterministic robot environment, and more generally even in the robot environment with bounded randomness.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
