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We propose a computational framework for replacing the repeated numerical solution of differential Riccati equations in finite-horizon Linear Quadratic Regulator (LQR) problems by a learned operator surrogate. Instead of solving a nonlinear…
The paper considers a class of multi-agent Markov decision processes (MDPs), in which the network agents respond differently (as manifested by the instantaneous one-stage random costs) to a global controlled state and the control actions of…
In this article, we consider a cooperative control problem involving a heterogeneous network of dynamically decoupled continuous-time linear plants. The (output-feedback) controllers for each plant may communicate with each other according…
Distributed optimal control is known to be challenging and can become intractable even for linear-quadratic regulator problems. In this work, we study a special class of such problems where distributed state feedback controllers can give…
User scheduling and multiuser multi-antenna (MU-MIMO) transmission are at the core of high rate data-oriented downlink schemes of the next-generation of cellular systems (e.g., LTE-Advanced). Scheduling selects groups of users according to…
Catastrophic interference is common in many network-based learning systems, and many proposals exist for mitigating it. Before overcoming interference we must understand it better. In this work, we provide a definition and novel measure of…
Engineering system design, viewed as a decision-making process, faces challenges due to complexity and uncertainty. In this paper, we present a framework proposing the use of the Deep Q-learning algorithm to optimize the design of…
In this paper, we present a deep reinforcement learning (RL) framework for iterative dialog policy optimization in end-to-end task-oriented dialog systems. Popular approaches in learning dialog policy with RL include letting a dialog agent…
It is known that reinforcement learning (RL) is data-hungry. To improve sample-efficiency of RL, it has been proposed that the learning algorithm utilize data from 'approximately similar' processes. However, since the process models are…
Traffic signal controllers play an essential role in today's traffic system. However, the majority of them currently is not sufficiently flexible or adaptive to generate optimal traffic schedules. In this paper we present an approach to…
Manipulate and control of the complex quantum system with high precision are essential for achieving universal fault tolerant quantum computing. For a physical system with restricted control resources, it is a challenge to control the…
In this paper, a novel robust tracking control scheme for a general class of discrete-time nonlinear systems affected by unknown bounded uncertainty is presented. By solving a parameterized optimal tracking control problem subject to the…
We analyze the closed-loop control performance of a networked control system that consists of $N$ independent linear feedback control loops, sharing a communication network with $M$ channels ($M<N$). A centralized scheduler, employing a…
We propose a data-driven control design method for nonlinear systems that builds on kernel-based interpolation. Under some assumptions on the system dynamics, kernel-based functions are built from data and a model of the system, along with…
With the development of experimental quantum technology, quantum control has attracted increasing attention due to the realization of controllable artificial quantum systems. However, because quantum-mechanical systems are often too…
Co-existence of 5G New Radio (5G-NR) with IoT devices is considered as a promising technique to enhance the spectral usage and efficiency of future cellular networks. In this paper, a unified framework has been proposed for allocating…
With the gradual advancement of a novel idea of the distributed control of the multiagent systems, an event-triggered control protocol has received significant research attention, especially in designing the controller for the nonlinear…
Various congestion control protocols have been designed to achieve high performance in different network environments. Modern online learning solutions that delegate the congestion control actions to a machine cannot properly converge in…
This work investigates the formal policy synthesis of continuous-state stochastic dynamic systems given high-level specifications in linear temporal logic. To learn an optimal policy that maximizes the satisfaction probability, we take a…
We study scheduling control of parallel processing networks in which some resources need to simultaneously collaborate to perform some activities and some resources multitask. Resource collaboration and multitasking give rise to…