3 citations · 7 across the 5 of their papers we have counts for
5 papers
SEABO: A Simple Search-Based Method for Offline Imitation Learning
Jiafei Lyu, Xiaoteng Ma, Le Wan +3
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 enviro…
Understanding What Affects the Generalization Gap in Visual Reinforcement Learning: Theory and Empirical Evidence
Jiafei Lyu, Le Wan, Xiu Li +1
Recently, there are many efforts attempting to learn useful policies for continuous control in visual reinforcement learning (RL). In this scenario, it is important to learn a gene…
Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple Reuse
Jiafei Lyu, Le Wan, Zongqing Lu +1
Sample efficiency is one of the most critical issues for online reinforcement learning (RL). Existing methods achieve higher sample efficiency by adopting model-based methods, Q-en…
Uncertainty-driven Trajectory Truncation for Data Augmentation in Offline Reinforcement Learning
Junjie Zhang, Jiafei Lyu, Xiaoteng Ma +4
Equipped with the trained environmental dynamics, model-based offline reinforcement learning (RL) algorithms can often successfully learn good policies from fixed-sized datasets, e…
State Advantage Weighting for Offline RL
Jiafei Lyu, Aicheng Gong, Le Wan +2
We present state advantage weighting for offline reinforcement learning (RL). In contrast to action advantage that we commonly adopt in QSA learning, we leverage state adv…