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Zhang, Zheng (author), Zhang, Dengyu (author), Zhang, Qingrui (author), Pan, W. (author), Hu, Tianjiang (author)Integrating rule-based policies into reinforcement learning promises to improve data efficiency and generalization in cooperative pursuit problems. However, most implementations do not properly distinguish the influence of neighboring robots in observation embedding or inter-robot interaction rules, leading to information loss and inefficient...journal article 2024
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Wei, Zeyong (author), Chen, Honghua (author), Nan, L. (author), Wang, Jun (author), Qin, Jing (author), Wei, Mingqiang (author)Current point cloud denoising (PCD) models optimize single networks, trying to make their parameters adaptive to each point in a large pool of point clouds. Such a denoising network paradigm neglects that different points are often corrupted by different levels of noise and they may convey different geometric structures. Thus, the intricacy of...journal article 2024
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Liu, Y. (author), Pan, W. (author)Machine learning can be effectively applied in control loops to make optimal control decisions robustly. There is increasing interest in using spiking neural networks (SNNs) as the apparatus for machine learning in control engineering because SNNs can potentially offer high energy efficiency, and new SNN-enabling neuromorphic hardware is being...journal article 2023
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Voogd, Kevin L. (author), Allamaa, Jean Pierre (author), Alonso-Mora, J. (author), Son, Tong Duy (author)Reinforcement learning (RL) is a promising solution for autonomous vehicles to deal with complex and uncertain traffic environments. The RL training process is however expensive, unsafe, and time-consuming. Algorithms are often developed first in simulation and then transferred to the real-world, leading to a common sim2real challenge where...journal article 2023
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Abolfazli, Amir (author), Spiegelberg, Jakob (author), Anand, A. (author), Palmer, Gregory (author)Configurable software systems have become increasingly popular as they enable customized software variants. The main challenge in dealing with configuration problems is that the number of possible configurations grows exponentially as the number of features increases. Therefore, algorithms for testing customized software have to deal with the...conference paper 2023
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Joseph, G. (author), Zhong, Chen (author), Gursoy, M. Cenk (author), Velipasalar, Senem (author), Varshney, Pramod (author)We address the problem of sequentially selecting and observing processes from a given set to find the anomalies among them. The decision-maker observes a subset of the processes at any given time instant and obtains a noisy binary indicator of whether or not the corresponding process is anomalous. We develop an anomaly detection algorithm that...journal article 2023
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Sun, D. (author), Jamshidnejad, A. (author), De Schutter, B.H.K. (author)Traffic control is essential to reduce congestion in both urban and freeway traffic networks. These control measures include ramp metering and variable speed limits for freeways, and traffic signal control for urban traffic. However, current traffic control methods are either too simple to respond to complex traffic environment, or too...journal article 2023
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Neustroev, G. (author)Sequential decision-making under uncertainty is an important branch of artificial intelligence research with a plethora of real-life applications. In this thesis, we generalize two fundamental properties of the decision-making process. First, we show that the theory on planning methods for finite spaces can be extended to infinite but countable...doctoral thesis 2022
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Sarkar, A. (author)Efforts to realize a sufficiently large controllable quantum processor are actively being pursued globally. These quantum devices are programmed by specifying the manipulation of quantum information via quantum algorithms. This doctoral research provides an application perspective to the design requirements of a quantum accelerator architecture....doctoral thesis 2022
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Adibi, M. (author), van der Woude, J.W. (author)In this article, we present a reinforcement learning-based scheme for secondary frequency control of lossy inverter-based microgrids. Compared with the existing methods in the literature, we relax the common restrictions on the system, i.e., being lossless, and the transmission lines and loads to have known constant impedances. The proposed...journal article 2022
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Wei, Lianzhen (author), Gong, Jianwei (author), Chen, Huiyan (author), Li, Z. (author), Gong, Cheng (author)To deal with the nonlinear interference caused by chassis movement and road surface undulations with the tracking and aiming of unmanned combat ground vehicles, a tracking and aiming adaptive control method for unmanned combat ground vehicles on the move based on reinforcement learning compensation is proposed. This method consists of a main...journal article 2022
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Stops, L. (author), Leenhouts, Roel (author), Gao, Q. (author), Schweidtmann, A.M. (author)Process synthesis experiences a disruptive transformation accelerated by artificial intelligence. We propose a reinforcement learning algorithm for chemical process design based on a state-of-the-art actor-critic logic. Our proposed algorithm represents chemical processes as graphs and uses graph convolutional neural networks to learn from...journal article 2022
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Li, Guoqiang (author), Gorges, Daniel (author), Wang, M. (author)In this paper a learning-based optimization method for online gear shift and velocity control is presented to reduce the fuel consumption and improve the driving comfort in a car-following process. The continuous traction force and the discrete gear shift are optimized jointly to improve both the powertrain operation and the longitudinal...journal article 2022
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Baccour, Emna (author), Mhaisen, N. (author), Abdellatif, Alaa Awad (author), Erbad, Aiman (author), Mohamed, Amr (author), Hamdi, Mounir (author), Guizani, Mohsen (author)Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems and speech processing applications to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive...journal article 2022
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Baldi, S. (author), Zhang, Z. (author), Liu, Di (author)We propose a new reinforcement learning method in the framework of Recursive Least Squares-Temporal Difference (RLS-TD). Instead of using the standard mechanism of eligibility traces (resulting in RLS-TD((Formula presented.))), we propose to use the forgetting factor commonly used in gradient-based or least-square estimation, and we show that...journal article 2022
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Moerland, Thomas M. (author), Broekens, D.J. (author), Plaat, Aske (author), Jonker, C.M. (author)Sequential decision making, commonly formalized as optimization of a Markov Decision Process, is a key challenge in artificial intelligence. Two successful approaches to MDP optimization are reinforcement learning and planning, which both largely have their own research communities. However, if both research fields solve the same problem,...journal article 2022
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Ribeiro, M.J. (author), Ellerbroek, Joost (author), Hoekstra, J.M. (author)Current predictions on future drone operations estimate that traffic density orders of magnitude will be higher than any observed in manned aviation. Such densities redirect the focus towards elements that can decrease conflict rate and severity, with special emphasis on airspace structures, an element that has been overlooked within...journal article 2022
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Ribeiro, M.J. (author), Ellerbroek, Joost (author), Hoekstra, J.M. (author)Current predictions for future operations with drones estimate traffic densities orders of magnitude higher than any observed in manned aviation. Such densities call for further research and innovation, in particular, into conflict detection and resolution without the need for human intervention. The layered airspace concept, where aircraft...journal article 2022
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Poplavskaya, K. (author)Balancing and redispatch are essential services for the security and stability of the electricity network. Balancing refers to continuously maintaining a balance between supply and demand through activating flexible resources. Redispatch refers to changing the dispatch of generators to remedy network congestion. The need for flexibility...doctoral thesis 2021
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Zhang, Q. (author), Pan, W. (author), Reppa, V. (author)This paper presents a novel model-reference reinforcement learning algorithm for the intelligent tracking control of uncertain autonomous surface vehicles with collision avoidance. The proposed control algorithm combines a conventional control method with reinforcement learning to enhance control accuracy and intelligence. In the proposed...journal article 2021
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