6 citations · 19 across the 7 of their papers we have counts for
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Policy Optimization for Markovian Jump Linear Quadratic Control: Gradient-Based Methods and Global Convergence
Joao Paulo Jansch-Porto, Bin Hu, Geir Dullerud
Recently, policy optimization for control purposes has received renewed attention due to the increasing interest in reinforcement learning. In this paper, we investigate the global…
Policy Learning of MDPs with Mixed Continuous/Discrete Variables: A Case Study on Model-Free Control of Markovian Jump Systems
Joao Paulo Jansch-Porto, Bin Hu, Geir Dullerud
Markovian jump linear systems (MJLS) are an important class of dynamical systems that arise in many control applications. In this paper, we introduce the problem of controlling unk…
Convergence Guarantees of Policy Optimization Methods for Markovian Jump Linear Systems
Joao Paulo Jansch-Porto, Bin Hu, Geir Dullerud
Recently, policy optimization for control purposes has received renewed attention due to the increasing interest in reinforcement learning. In this paper, we investigate the conver…
Policy Optimization for Linear Control with Robustness Guarantee: Implicit Regularization and Global Convergence
Kaiqing Zhang, Bin Hu, Tamer Başar
Policy optimization (PO) is a key ingredient for reinforcement learning (RL). For control design, certain constraints are usually enforced on the policies to optimize, accounting f…
Optimal Co-design of Industrial Networked Control Systems with State-dependent Correlated Fading Channels
Bin Hu, Tua A. Tamba
This paper examines a co-design problem for industrial networked control systems (NCS) whereby physical systems are controlled over wireless fading channels. In particular, the con…
Co-design of Safe and Efficient Networked Control Systems in Factory Automation with State-dependent Wireless Fading Channels
Bin Hu, Yebin Wang, Philip Orlik +2
In factory automation, heterogeneous manufacturing processes need to be coordinated over wireless networks to achieve safety and efficiency. These wireless networks, however, are i…