Search Results for author:
Found 5 papers, 2 papers with code
SEABO: A Simple Search-Based Method for Offline Imitation Learning
Offline reinforcement learning (RL) has attracted much attention due to its ability in learning from static offline datasets and eliminating the need of interacting with the environment.
Understanding What Affects Generalization Gap in Visual Reinforcement Learning: Theory and Empirical Evidence
Recently, there are many efforts attempting to learn useful policies for continuous control in visual reinforcement learning (RL).
Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple Reuse
Empirical results show that SMR significantly boosts the sample efficiency of the base methods across most of the evaluated tasks without any hyperparameter tuning or additional tricks.
Uncertainty-driven Trajectory Truncation for Data Augmentation in Offline Reinforcement Learning
To empirically show the advantages of TATU, we first combine it with two classical model-based offline RL algorithms, MOPO and COMBO.
State Advantage Weighting for Offline RL
We present state advantage weighting for offline reinforcement learning (RL).
Cannot find the paper you are looking for? You can
Submit a new open access paper.
