19 citations · 26 across the 5 of their papers we have counts for
8 papers
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving
Eli Bronstein, Mark Palatucci, Dominik Notz +14
We demonstrate the first large-scale application of model-based generative adversarial imitation learning (MGAIL) to the task of dense urban self-driving. We augment standard MGAIL…
Slot Contrastive Networks: A Contrastive Approach for Representing Objects
Evan Racah, Sarath Chandar
Unsupervised extraction of objects from low-level visual data is an important goal for further progress in machine learning. Existing approaches for representing objects without la…
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning
Harm van Seijen, Hadi Nekoei, Evan Racah +1
Deep model-based Reinforcement Learning (RL) has the potential to substantially improve the sample-efficiency of deep RL. While various challenges have long held it back, a number…
Supervise Thyself: Examining Self-Supervised Representations in Interactive Environments
Evan Racah, Christopher Pal
Self-supervised methods, wherein an agent learns representations solely by observing the results of its actions, become crucial in environments which do not provide a dense reward…
Unsupervised State Representation Learning in Atari
Ankesh Anand, Evan Racah, Sherjil Ozair +3
State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of…
Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC
Wahid Bhimji, Steven Andrew Farrell, Thorsten Kurth +3
There has been considerable recent activity applying deep convolutional neural nets (CNNs) to data from particle physics experiments. Current approaches on ATLAS/CMS have largely f…