research
∙
05/26/2023
Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation
In recent years, domains such as natural language processing and image r...
share
research
∙
10/05/2022
Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning
We consider how to most efficiently leverage teleoperator time to collec...
research
∙
06/02/2022
Incorporating Explicit Uncertainty Estimates into Deep Offline Reinforcement Learning
Most theoretically motivated work in the offline reinforcement learning ...
research
∙
06/02/2022
When does return-conditioned supervised learning work for offline reinforcement learning?
Several recent works have proposed a class of algorithms for the offline...
research
∙
01/31/2022
Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning
Recent progress in deep learning has relied on access to large and diver...
research
∙
12/02/2021
Quantile Filtered Imitation Learning
We introduce quantile filtered imitation learning (QFIL), a novel policy...
research
∙
06/16/2021
Offline RL Without Off-Policy Evaluation
Most prior approaches to offline reinforcement learning (RL) have taken ...
research
∙
09/15/2020
Evaluating representations by the complexity of learning low-loss predictors
We consider the problem of evaluating representations of data for use in...
research
∙
06/27/2020
Overfitting and Optimization in Offline Policy Learning
We consider the task of policy learning from an offline dataset generate...
research
∙
11/01/2019
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration
We consider the exploration-exploitation dilemma in finite-horizon reinf...
research
∙
05/29/2019
