32 citations · 62 across the 10 of their papers we have counts for
10 papers
On the Optimization Landscape of Observer-based Dynamic Linear Quadratic Control
Jingliang Duan, Jie Li, Yinsong Ma +5
Understanding the optimization landscape of linear quadratic regulation (LQR) problems is fundamental to the design of efficient reinforcement learning solutions. Recent work has m…
One-Step Sampler for Boltzmann Distributions via Drifting
Wenhan Cao, Keyu Yan, Lin Zhao
We present a drifting-based framework for amortized sampling of Boltzmann distributions defined by energy functions. The method trains a one-step neural generator by projecting sam…
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…
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…
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…
On the Optimization Landscape of Dynamic Output Feedback: A Case Study for Linear Quadratic Regulator
Jingliang Duan, Wenhan Cao, Yang Zheng +1
The convergence of policy gradient algorithms in reinforcement learning hinges on the optimization landscape of the underlying optimal control problem. Theoretical insights into th…
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…
On the Optimization Landscape of Dynamic Output Feedback Linear Quadratic Control
Jingliang Duan, Wenhan Cao, Yang Zheng +1
The convergence of policy gradient algorithms hinges on the optimization landscape of the underlying optimal control problem. Theoretical insights into these algorithms can often b…
Encoding Distributional Soft Actor-Critic for Autonomous Driving in Multi-lane Scenarios
Jingliang Duan, Yangang Ren, Fawang Zhang +5
In this paper, we propose a new reinforcement learning (RL) algorithm, called encoding distributional soft actor-critic (E-DSAC), for decision-making in autonomous driving. Unlike…
Optimization Landscape of Gradient Descent for Discrete-time Static Output Feedback
Jingliang Duan, Jie Li, Shengbo Eben Li +1
In this paper, we analyze the optimization landscape of gradient descent methods for static output feedback (SOF) control of discrete-time linear time-invariant systems with quadra…