Toward a theoretical foundation of policy optimization for learning control policies
Gradient-based methods have been widely used for system design and optimization in
diverse application domains. Recently, there has been a renewed interest in studying …
diverse application domains. Recently, there has been a renewed interest in studying …
Online robust reinforcement learning with model uncertainty
Robust reinforcement learning (RL) is to find a policy that optimizes the worst-case
performance over an uncertainty set of MDPs. In this paper, we focus on model-free robust …
performance over an uncertainty set of MDPs. In this paper, we focus on model-free robust …
[图书][B] Reinforcement learning for sequential decision and optimal control
SE Li - 2023 - Springer
Since the beginning of the 21st century, artificial intelligence (AI) has been reshaping almost
all areas of human society, which has high potential to spark the fourth industrial revolution …
all areas of human society, which has high potential to spark the fourth industrial revolution …
Improved sample complexity bounds for distributionally robust reinforcement learning
We consider the problem of learning a control policy that is robust against the parameter
mismatches between the training environment and testing environment. We formulate this as …
mismatches between the training environment and testing environment. We formulate this as …
The confluence of networks, games, and learning a game-theoretic framework for multiagent decision making over networks
Multiagent decision making over networks has recently attracted an exponentially growing
number of researchers from the systems and control community. The area has gained …
number of researchers from the systems and control community. The area has gained …
Complexity of Derivative-Free Policy Optimization for Structured Control
The applications of direct policy search in reinforcement learning and continuous control
have received increasing attention. In this work, we present novel theoretical results on the …
have received increasing attention. In this work, we present novel theoretical results on the …
Robust reinforcement learning as a stackelberg game via adaptively-regularized adversarial training
Robust Reinforcement Learning (RL) focuses on improving performances under model
errors or adversarial attacks, which facilitates the real-life deployment of RL agents. Robust …
errors or adversarial attacks, which facilitates the real-life deployment of RL agents. Robust …
Derivative-free policy optimization for linear risk-sensitive and robust control design: Implicit regularization and sample complexity
Direct policy search serves as one of the workhorses in modern reinforcement learning (RL),
and its applications in continuous control tasks have recently attracted increasing attention …
and its applications in continuous control tasks have recently attracted increasing attention …
Global Convergence of Direct Policy Search for State-Feedback Robust Control: A Revisit of Nonsmooth Synthesis with Goldstein Subdifferential
Direct policy search has been widely applied in modern reinforcement learning and
continuous control. However, the theoretical properties of direct policy search on nonsmooth …
continuous control. However, the theoretical properties of direct policy search on nonsmooth …
Robust policy iteration of uncertain interconnected systems with imperfect data
This paper investigates the robust optimal control problem of a class of continuous-time,
partially linear, interconnected systems. In addition to the dynamic uncertainties resulted …
partially linear, interconnected systems. In addition to the dynamic uncertainties resulted …