38 citations · 65 across the 10 of their papers we have counts for
10 papers
FastDSAC: Enhancing Policy Plasticity via Constrained Exploration for Scalable Humanoid Locomotion
Guanchen Lu, Yajuan Dun, Yi Zhou +4
Scalable reinforcement learning has popularized high-throughput sampling architectures, which significantly compresses the training time for off-policy methods in robotic locomotio…
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…
Policy Bifurcation in Safe Reinforcement Learning
Wenjun Zou, Yao Lyu, Jie Li +7
Safe reinforcement learning (RL) offers advanced solutions to constrained optimal control problems. Existing studies in safe RL implicitly assume continuity in policy functions, wh…
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…
Bridging the Gap between Newton-Raphson Method and Regularized Policy Iteration
Zeyang Li, Chuxiong Hu, Yunan Wang +4
Regularization is a cornerstone of modern reinforcement learning. Regularized policy iteration (RPI) provides a fundamental scheme for solving regularized Markov decision processes…
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…
Approximate Optimal Filter for Linear Gaussian Time-invariant Systems
Kaiming Tang, Shengbo Eben Li, Yuming Yin +4
State estimation is critical to control systems, especially when the states cannot be directly measured. This paper presents an approximate optimal filter, which enables to use pol…
Mixed Policy Gradient: off-policy reinforcement learning driven jointly by data and model
Yang Guan, Jingliang Duan, Shengbo Eben Li +3
Reinforcement learning (RL) shows great potential in sequential decision-making. At present, mainstream RL algorithms are data-driven, which usually yield better asymptotic perform…
Direct and indirect reinforcement learning
Yang Guan, Shengbo Eben Li, Jingliang Duan +4
Reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks. In this paper, we classify RL into dir…
Relaxed Actor-Critic with Convergence Guarantees for Continuous-Time Optimal Control of Nonlinear Systems
Jingliang Duan, Jie Li, Qiang Ge +4
This paper presents the Relaxed Continuous-Time Actor-critic (RCTAC) algorithm, a method for finding the nearly optimal policy for nonlinear continuous-time (CT) systems with known…