5.6k citations · 16.3k across the 53 of their papers we have counts for
5 papers · 1 filter
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning
Denis Yarats, Rob Fergus, Alessandro Lazaric +1
We present DrQ-v2, a model-free reinforcement learning (RL) algorithm for visual continuous control. DrQ-v2 builds on DrQ, an off-policy actor-critic approach that uses data augmen…
Imitation by Predicting Observations
Andrew Jaegle, Yury Sulsky, Arun Ahuja +3
Imitation learning enables agents to reuse and adapt the hard-won expertise of others, offering a solution to several key challenges in learning behavior. Although it is easy to ob…
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…
Reinforcement Learning with Prototypical Representations
Denis Yarats, Rob Fergus, Alessandro Lazaric +1
Learning effective representations in image-based environments is crucial for sample efficient Reinforcement Learning (RL). Unfortunately, in RL, representation learning is confoun…
Decoupling Value and Policy for Generalization in Reinforcement Learning
Roberta Raileanu, Rob Fergus
Standard deep reinforcement learning algorithms use a shared representation for the policy and value function, especially when training directly from images. However, we argue that…