131 citations · 279 across the 5 of their papers we have counts for
9 papers
Offline Reinforcement Learning with Implicit Q-Learning
Ilya Kostrikov, Ashvin Nair, Sergey Levine
Offline reinforcement learning requires reconciling two conflicting aims: learning a policy that improves over the behavior policy that collected the dataset, while at the same tim…
Offline Reinforcement Learning with Fisher Divergence Critic Regularization
Ilya Kostrikov, Jonathan Tompson, Rob Fergus +1
Many modern approaches to offline Reinforcement Learning (RL) utilize behavior regularization, typically augmenting a model-free actor critic algorithm with a penalty measuring div…
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…
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…
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…
Imitation Learning via Off-Policy Distribution Matching
Ilya Kostrikov, Ofir Nachum, Jonathan Tompson
When performing imitation learning from expert demonstrations, distribution matching is a popular approach, in which one alternates between estimating distribution ratios and then…