17 citations · 33 across the 5 of their papers we have counts for
7 papers
Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-Critic
Tianying Ji, Yu Luo, Fuchun Sun +3
Learning high-quality -value functions plays a key role in the success of many modern off-policy deep reinforcement learning (RL) algorithms. Previous works primarily focus on a…
PROTO: Iterative Policy Regularized Offline-to-Online Reinforcement Learning
Jianxiong Li, Xiao Hu, Haoran Xu +3
Offline-to-online reinforcement learning (RL), by combining the benefits of offline pretraining and online finetuning, promises enhanced sample efficiency and policy performance. H…
Query-Policy Misalignment in Preference-Based Reinforcement Learning
Xiao Hu, Jianxiong Li, Xianyuan Zhan +2
Preference-based reinforcement learning (PbRL) provides a natural way to align RL agents' behavior with human desired outcomes, but is often restrained by costly human feedback. To…
Efficient Robotic Manipulation Through Offline-to-Online Reinforcement Learning and Goal-Aware State Information
Jin Li, Xianyuan Zhan, Zixu Xiao +1
End-to-end learning robotic manipulation with high data efficiency is one of the key challenges in robotics. The latest methods that utilize human demonstration data and unsupervis…
Offline Reinforcement Learning with Soft Behavior Regularization
Haoran Xu, Xianyuan Zhan, Jianxiong Li +1
Most prior approaches to offline reinforcement learning (RL) utilize \textit{behavior regularization}, typically augmenting existing off-policy actor critic algorithms with a penal…
CSCAD: Correlation Structure-based Collective Anomaly Detection in Complex System
Huiling Qin, Xianyuan Zhan, Yu Zheng
Detecting anomalies in large complex systems is a critical and challenging task. The difficulties arise from several aspects. First, collecting ground truth labels or prior knowled…