413 citations · 1.4k across the 26 of their papers we have counts for
Showing 2020Show all
3 papers · 1 filter
cs.LG2020★ 13 cited
Statistical Bootstrapping for Uncertainty Estimation in Off-Policy Evaluation
Ilya Kostrikov, Ofir Nachum
In reinforcement learning, it is typical to use the empirically observed transitions and rewards to estimate the value of a policy via either model-based or Q-fitting approaches. A…
cs.LG2020★ 52 cited
Automatic Data Augmentation for Generalization in Deep Reinforcement Learning
Roberta Raileanu, Max Goldstein, Denis Yarats +2
Deep reinforcement learning (RL) agents often fail to generalize to unseen scenarios, even when they are trained on many instances of semantically similar environments. Data augmen…
cs.LG2020★ 131 cited
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
Ilya Kostrikov, Denis Yarats, Rob Fergus
We propose a simple data augmentation technique that can be applied to standard model-free reinforcement learning algorithms, enabling robust learning directly from pixels without…