5.6k citations · 16.3k across the 53 of their papers we have counts for
5 papers · 1 filter
Fast Adaptation via Policy-Dynamics Value Functions
Roberta Raileanu, Max Goldstein, Arthur Szlam +1
Standard RL algorithms assume fixed environment dynamics and require a significant amount of interaction to adapt to new environments. We introduce Policy-Dynamics Value Functions…
Empirically Verifying Hypotheses Using Reinforcement Learning
Kenneth Marino, Rob Fergus, Arthur Szlam +1
This paper formulates hypothesis verification as an RL problem. Specifically, we aim to build an agent that, given a hypothesis about the dynamics of the world, can take actions to…
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…
Energy-based models for atomic-resolution protein conformations
Yilun Du, Joshua Meier, Jerry Ma +2
We propose an energy-based model (EBM) of protein conformations that operates at atomic scale. The model is trained solely on crystallized protein data. By contrast, existing appro…
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…