21 citations · 37 across the 14 of their papers we have counts for
4 papers · 1 filter
Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning
David Brandfonbrener, Stephen Tu, Avi Singh +4
We consider how to most efficiently leverage teleoperator time to collect data for learning robust image-based value functions and policies for sparse reward robotic tasks. To acco…
Incorporating Explicit Uncertainty Estimates into Deep Offline Reinforcement Learning
David Brandfonbrener, Remi Tachet des Combes, Romain Laroche
Most theoretically motivated work in the offline reinforcement learning setting requires precise uncertainty estimates. This requirement restricts the algorithms derived in that wo…
When does return-conditioned supervised learning work for offline reinforcement learning?
David Brandfonbrener, Alberto Bietti, Jacob Buckman +2
Several recent works have proposed a class of algorithms for the offline reinforcement learning (RL) problem that we will refer to as return-conditioned supervised learning (RCSL).…
Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning
Denis Yarats, David Brandfonbrener, Hao Liu +4
Recent progress in deep learning has relied on access to large and diverse datasets. Such data-driven progress has been less evident in offline reinforcement learning (RL), because…