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Automatic enhancement of vascular configuration for self-healing concrete through reinforcement learning approach

Wan, Z. (author), Xu, Y. (author), Chang, Z. (author), Liang, M. (author), Šavija, B. (author)

Vascular self-healing concrete (SHC) has great potential to mitigate the environmental impact of the construction industry by increasing the durability of structures. Designing concrete with high initial mechanical properties by searching a specific arrangement of vascular structure is of great importance. Herein, an automatic optimization...

journal article 2024

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Approximate dynamic programming for constrained linear systems: A piecewise quadratic approximation approach

He, K. (author), Shi, S. (author), van den Boom, A.J.J. (author), De Schutter, B.H.K. (author)

Approximate dynamic programming (ADP) faces challenges in dealing with constraints in control problems. Model predictive control (MPC) is, in comparison, well-known for its accommodation of constraints and stability guarantees, although its computation is sometimes prohibitive. This paper introduces an approach combining the two methodologies...

journal article 2024

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Learning-Based Multi-UAV Flocking Control With Limited Visual Field and Instinctive Repulsion

Bai, Chengchao (author), Yan, Peng (author), Piao, Haiyin (author), Pan, W. (author), Guo, Jifeng (author)

This article explores deep reinforcement learning (DRL) for the flocking control of unmanned aerial vehicle (UAV) swarms. The flocking control policy is trained using a centralized-learning-decentralized-execution (CTDE) paradigm, where a centralized critic network augmented with additional information about the entire UAV swarm is utilized...

journal article 2024

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Subtask-masked curriculum learning for reinforcement learning with application to UAV maneuver decision-making

Hou, Yueqi (author), Liang, Xiaolong (author), Lv, Maolong (author), Yang, Q. (author), Li, Y. (author)

Unmanned Aerial Vehicle (UAV) maneuver strategy learning remains a challenge when using Reinforcement Learning (RL) in this sparse reward task. In this paper, we propose Subtask-Masked curriculum learning for RL (SUBMAS-RL), an efficient RL paradigm that implements curriculum learning and knowledge transfer for UAV maneuver scenarios...

journal article 2023

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EQRSRL: an energy-aware and QoS-based routing schema using reinforcement learning in IoMT

Nazari, Amin (author), Kordabadi, Mojtaba (author), Mohammadi, R. (author), Lal, C. (author)

Internet of Medical Thing (IoMT) is an emerging technology in healthcare that can be used to realize a wide variety of medical applications. It improves people’s quality of life and makes it easier to care for the sick individuals in an efficient and safe manner. To do this, IoMT leverages the capabilities of some new technologies including...

journal article 2023

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Teacher-apprentices RL (TARL): leveraging complex policy distribution through generative adversarial hypernetwork in reinforcement learning

Tang, Shi Yuan (author), Irissappane, Athirai A. (author), Oliehoek, F.A. (author), Zhang, Jie (author)

Typically, a Reinforcement Learning (RL) algorithm focuses in learning a single deployable policy as the end product. Depending on the initialization methods and seed randomization, learning a single policy could possibly leads to convergence to different local optima across different runs, especially when the algorithm is sensitive to hyper...

journal article 2023

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Hierarchical Motion Planning and Tracking for Autonomous Vehicles Using Global Heuristic Based Potential Field and Reinforcement Learning Based Predictive Control

Du, Guodong (author), Zou, Yuan (author), Zhang, Xudong (author), Li, Z. (author), Liu, Qi (author)

The autonomous vehicle is widely applied in various ground operations, in which motion planning and tracking control are becoming the key technologies to achieve autonomous driving. In order to further improve the performance of motion planning and tracking control, an efficient hierarchical framework containing motion planning and tracking...

journal article 2023

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Fleet planning under demand and fuel price uncertainty using actor–critic reinforcement learning

Geursen, Isaak L. (author), Santos, Bruno F. (author), Yorke-Smith, N. (author)

Current state-of-the-art airline planning models face computational limitations, restricting the operational applicability to problems of representative sizes. This is particularly the case when considering the uncertainty necessarily associated with the long-term plan of an aircraft fleet. Considering the growing interest in the application of...

journal article 2023

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A new reinforcement learning-based variable speed limit control approach to improve traffic efficiency against freeway jam waves

Han, Yu (author), Hegyi, A. (author), Zhang, Le (author), He, Zhengbing (author), Chung, Edward (author), Liu, Pan (author)

Conventional reinforcement learning (RL) models of variable speed limit (VSL) control systems (and traffic control systems in general) cannot be trained in real traffic process because new control actions are usually explored randomly, which may result in high costs (delays) due to exploration and learning. For this reason, existing RL-based...

journal article 2022

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Influence-aware memory architectures for deep reinforcement learning in POMDPs

Suau, M. (author), He, J. (author), Congeduti, E. (author), Starre, R.A.N. (author), Czechowski, A.T. (author), Oliehoek, F.A. (author)

Due to its perceptual limitations, an agent may have too little information about the environment to act optimally. In such cases, it is important to keep track of the action-observation history to uncover hidden state information. Recent deep reinforcement learning methods use recurrent neural networks (RNN) to memorize past observations....

journal article 2022

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Global synchromodal shipment matching problem with dynamic and stochastic travel times: a reinforcement learning approach

Guo, W. (author), Atasoy, B. (author), Negenborn, R.R. (author)

Global synchromodal transportation involves the movement of container shipments between inland terminals located in different continents using ships, barges, trains, trucks, or any combination among them through integrated planning at a network level. One of the challenges faced by global operators is the matching of accepted shipments with...

journal article 2022

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Community energy storage operation via reinforcement learning with eligibility traces

Salazar Duque, Edgar Mauricio (author), Giraldo, Juan S. (author), Vergara Barrios, P.P. (author), Nguyen, Phuong (author), van der Molen, Anne (author), Slootweg, Han (author)

The operation of a community energy storage system (CESS) is challenging due to the volatility of photovoltaic distributed generation, electricity consumption, and energy prices. Selecting the optimal CESS setpoints during the day is a sequential decision problem under uncertainty, which can be solved using dynamic learning methods. This...

journal article 2022

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Learning to run a power network with trust

Marot, Antoine (author), Donnot, Benjamin (author), Chaouache, Karim (author), Kelly, Adrian (author), Huang, Qiuhua (author), Hossain, Ramij Raja (author), Cremer, Jochen (author)

Artificial agents are promising for real-time power network operations, particularly, to compute remedial actions for congestion management. However, due to high reliability requirements, purely autonomous agents will not be deployed any time soon and operators will be in charge of taking action for the foreseeable future. Aiming at designing...

journal article 2022

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Aircraft maintenance check scheduling using reinforcement learning

Andrade, Pedro (author), Silva, Catarina (author), Ribeiro, Bernardete (author), Santos, Bruno F. (author)

This paper presents a Reinforcement Learning (RL) approach to optimize the long-term scheduling of maintenance for an aircraft fleet. The problem considers fleet status, maintenance capacity, and other maintenance constraints to schedule hangar checks for a specified time horizon. The checks are scheduled within an interval, and the goal is...

journal article 2021

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Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles

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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Symbolic Regression Methods for Reinforcement Learning

Kubalik, Jiri (author), Derner, Erik (author), Zegklitz, Jan (author), Babuska, R. (author)

Reinforcement learning algorithms can solve dynamic decision-making and optimal control problems. With continuous-valued state and input variables, reinforcement learning algorithms must rely on function approximators to represent the value function and policy mappings. Commonly used numerical approximators, such as neural networks or basis...

journal article 2021

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Secure Control for Cyber-Physical Systems under Malicious Attacks

Wu, Chengwei (author), Yao, Weiran (author), Pan, W. (author), Sun, Guanghui (author), Liu, Jianxing (author), Wu, Ligang (author)

This article investigates the secure control problem for cyber-physical systems when the malicious data are injected into the cyber realm, which directly connects to the actuators. Based on moving target defense (MTD) and reinforcement learning, we propose a novel proactive and reactive defense control scheme. First, the system (A,B) is...

journal article 2021

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Reinforcement learning control of constrained dynamic systems with uniformly ultimate boundedness stability guarantee

Han, Minghao (author), Tian, Yuan (author), Zhang, Lixian (author), Wang, J. (author), Pan, W. (author)

Reinforcement learning (RL) is promising for complicated stochastic nonlinear control problems. Without using a mathematical model, an optimal controller can be learned from data evaluated by certain performance criteria through trial-and-error. However, the data-based learning approach is notorious for not guaranteeing stability, which is...

journal article 2021

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Learning Optimal Controllers for Linear Systems with Multiplicative Noise via Policy Gradient

Gravell, Benjamin (author), Mohajerin Esfahani, P. (author), Summers, Tyler H. (author)

The linear quadratic regulator (LQR) problem has reemerged as an important theoretical benchmark for reinforcement learning-based control of complex dynamical systems with continuous state and action spaces. In contrast with nearly all recent work in this area, we consider multiplicative noise models, which are increasingly relevant because...

journal article 2021

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Learning Tracking Control for Cyber-Physical Systems

Wu, C. (author), Pan, W. (author), Sun, Guanghui (author), Liu, Jianxing (author), Wu, Ligang (author)

This paper investigates the problem of optimal tracking control for cyber-physical systems (CPS) when the cyber realm is attacked by denial-of-service (DoS) attacks which can prevent the control signal transmitting to the actuator. Attention is focused on how to design the optimal tracking control scheme without using the system dynamics and...

journal article 2021