10 citations · 17 across the 11 of their papers we have counts for
11 papers
OpenClaw-RL: Train Any Agent Simply by Talking
Yinjie Wang, Xuyang Chen, Xiaolong Jin +2
Every agent interaction generates a next-state signal, namely the user reply, tool output, terminal or GUI state change that follows each action, yet no existing agentic RL system…
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…
Optimization Landscape of Policy Gradient Methods for Discrete-time Static Output Feedback
Jingliang Duan, Jie Li, Xuyang Chen +3
In recent times, significant advancements have been made in delving into the optimization landscape of policy gradient methods for achieving optimal control in linear time-invarian…
Trust-Region Neural Moving Horizon Estimation for Robots
Bingheng Wang, Xuyang Chen, Lin Zhao
Accurate disturbance estimation is essential for safe robot operations. The recently proposed neural moving horizon estimation (NeuroMHE), which uses a portable neural network to m…
Efficient Q-Learning over Visit Frequency Maps for Multi-agent Exploration of Unknown Environments
Xuyang Chen, Ashvin N. Iyer, Zixing Wang +1
The robot exploration task has been widely studied with applications spanning from novel environment mapping to item delivery. For some time-critical tasks, such as rescue catastro…
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…