Search Results for author:
Found 2 papers, 0 papers with code
Deep Modeling of Non-Gaussian Aleatoric Uncertainty
Deep learning offers promising new ways to accurately model aleatoric uncertainty in robotic estimation systems, particularly when the uncertainty distributions do not conform to traditional assumptions of being fixed and Gaussian.
Symmetry Detection in Trajectory Data for More Meaningful Reinforcement Learning Representations
Knowledge of the symmetries of reinforcement learning (RL) systems can be used to create compressed and semantically meaningful representations of a low-level state space.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
