10 citations · 17 across the 11 of their papers we have counts for
7 papers · 1 filter
Target-Aligned Fusion for Decision-Sequence Learning under Dynamics Shift
Guojian Wang, Quinson Hon, Xuyang Chen +1
External trajectories can improve offline decision-sequence learning, but dynamics shift may make some source subsequences inconsistent with the target environment. We study how to…
Global Optimality of Single-Timescale Actor-Critic under Continuous State-Action Space: A Study on Linear Quadratic Regulator
Xuyang Chen, Jingliang Duan, Lin Zhao
Actor-critic methods have achieved state-of-the-art performance in various challenging tasks. However, theoretical understandings of their performance remain elusive and challengin…
Taming OOD Actions for Offline Reinforcement Learning: An Advantage-Based Approach
Xuyang Chen, Keyu Yan, Wenhan Cao +1
Offline reinforcement learning (RL) learns policies from fixed datasets without online interactions, but suffers from distribution shift, causing inaccurate evaluation and overesti…
VIPO: Value Function Inconsistency Penalized Offline Reinforcement Learning
Xuyang Chen, Keyu Yan, Guojian Wang +1
Offline reinforcement learning (RL) learns effective policies from pre-collected datasets, offering a practical solution for applications where online interactions are risky or cos…
Preference-Guided Reinforcement Learning for Efficient Exploration
Guojian Wang, Jianxiang Liu, Xinyuan Li +4
In this paper, we investigate preference-based reinforcement learning (PbRL), which enables reinforcement learning (RL) agents to learn from human feedback. This is particularly va…
Finite-time analysis of single-timescale actor-critic
Xuyang Chen, Lin Zhao
Actor-critic methods have achieved significant success in many challenging applications. However, its finite-time convergence is still poorly understood in the most practical singl…
Global Convergence of Two-timescale Actor-Critic for Solving Linear Quadratic Regulator
Xuyang Chen, Jingliang Duan, Yingbin Liang +1
The actor-critic (AC) reinforcement learning algorithms have been the powerhouse behind many challenging applications. Nevertheless, its convergence is fragile in general. To study…