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Data-Model Hybrid-Driven Safe reinforcement learning for adaptive Avoidance Control Against Unsafe Moving Zones

ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2025年 PP卷 PP页

作者： Wang, Ke Mu, Chaoxu Zhang, Anguo Sun, Changyin Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230026 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Peoples R China

With the gradual application of reinforcement learning (RL), safety has emerged as a paramount concern. This article presents a novel data-model hybrid-driven safe RL (SRL) scheme to address the challenge of avoidance control in the operation domain containing multiple moving unsafe zones. First, the avoidance problem is transformed into the optimal control problem of an augmented system by encoding a barrier function (BF) term into the cost function. Then, using the idea of integral RL (IRL), an adaptive learning algorithm is proposed for generating safe control policies, in which the actor-critic neural network (NN) structure is established with the aid of state-following (StaF) kernel function. The policy iteration process is executed by this structure;specifically, the critic network undergoes gradient-descent adaptation, while the actor network employs gradient projection updating. Particularly, via a state extrapolation technique, both real-time experience and simulated experience are utilized in the learning process. Next, closed-loop stability and weight convergence are theoretically substantiated. Finally, the effectiveness of the proposed scheme is demonstrated on a single integrator system, a nonlinear numerical system, and a unicycle kinematic system;besides, its advantages over the existing control methods are illustrated by comparisons.

关键词： Safety Optimal control Cost function Artificial neural networks Kernel Adaptation models reinforcement learning Mathematical models Extrapolation Data models Actor-critic neural network (NN) adaptive dynamic programming (ADP) avoidance control policy iteration safe reinforcement learning (SRL) state extrapolation

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Task-priority Intermediated Hierarchical Distributed Policies: reinforcement learning of adaptive Multi-robot Cooperative Transport

Task-priority Intermediated Hierarchical Distributed Policie...

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ieee/SICE International symposium on System Integration

作者： Yusei Naito Tomohiko Jimbo Tadashi Odashima Takamitsu Matsubara R-Frontier Division Frontier Research Center Toyota Motor Corporation Aichi Japan Graduate School of Information Science Nara Institute of Science and Technology (NAIST) Nara Japan Toyota Central R&D LABS. Inc. Aichi Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

Multi-robot cooperative transport is crucial in logistics, housekeeping, and disaster response. However, it poses significant challenges in environments where objects of various weights are mixed and the number of robots and objects varies. This paper presents Task-priority Intermediated Hierarchical Distributed Policies (TIHDP), a multi-agent reinforcement learning (RL) framework that addresses these challenges through a hierarchical policy structure. TIHDP consists of three layers: task allocation policy (higher layer), dynamic task priority (intermediate layer), and robot control policy (lower layer). Whereas the dynamic task priority layer can manipulate the priority of any object to be transported by receiving global object information and communicating with other robots, the task allocation and robot control policies are restricted by local observations/actions so that they are not affected by changes in the number of objects and robots. Through simulations and real-robot demonstrations, TIHDP shows promising adaptability and performance of the learned multi-robot cooperative transport, even in environments with varying numbers of robots and objects.

关键词： Adaptation models adaptive systems Disasters Robot control reinforcement learning System integration dynamic scheduling Resource management Manipulator dynamics Logistics

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LEACH-RLC: Enhancing IoT Data Transmission with Optimized Clustering and reinforcement learning

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ieee Internet of Things Journal 2025年

作者： Jurado-Lasso, F. Fernando Jurado, J.F. Fafoutis, Xenofon Technical University of Denmark Embedded Systems Engineering Section Dtu Compute Lyngby2800 Denmark Universidad Nacional de Colombia Sede Palmira Faculty of Engineering and Administration Department of Basic Science Palmira763531 Colombia

Wireless Sensor Networks (WSNs) play a pivotal role in enabling Internet of Things (IoT) devices with sensing and actuation capabilities. Operating in remote and resourceconstrained environments, these IoT devices face challenges related to energy consumption, crucial for network longevity. Existing clustering protocols often suffer from high control overhead, inefficient cluster formation, and poor adaptability to dynamic network conditions, leading to suboptimal data transmission and reduced network lifetime. This paper introduces Low-Energy adaptive Clustering Hierarchy with reinforcement learning-based Controller (LEACH-RLC), a novel clustering protocol designed to address these limitations by employing a Mixed Integer Linear programming (MILP) approach for strategic selection of Cluster Heads (CHs) and node-to-cluster assignments. Additionally, it integrates a reinforcement learning (RL) agent to minimize control overhead by learning optimal timings for generating new clusters. LEACH-RLC aims to balance control overhead reduction without compromising overall network performance. Through extensive simulations, this paper investigates the frequency and opportune moments for generating new clustering solutions. Results demonstrate the superior performance of LEACH-RLC over state-of-theart protocols, showcasing enhanced network lifetime, reduced average energy consumption, and minimized control overhead. The proposed protocol contributes to advancing the efficiency and adaptability of WSNs, addressing critical challenges in IoT deployments. © 2014 ieee.

关键词： reinforcement learning

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Resilient Control Under Denial-of-service and Uncertainty: An adaptive dynamic programming Approach

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ieee Transactions on Automatic Control 2025年

作者： Gao, Weinan Jiang, Zhong-Ping Chai, Tianyou Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China New York University Department of Electrical and Computer Engineering Tandon School of Engineering BrooklynNY11201 United States

In this paper, a new framework for the resilient control of continuous-time linear systems under denial-of-service (DoS) attacks and system uncertainty is presented. Integrating techniques from reinforcement learning and output regulation theory, it is shown that resilient optimal controllers can be learned directly from real-time state and input data collected from the systems subjected to attacks. Sufficient conditions are given under which the closed-loop system remains stable given any upper bound of DoS attack duration. Simulation results are used to demonstrate the efficacy of the proposed learning-based framework for resilient control under DoS attacks and model uncertainty. © 1963-2012 ieee.

关键词： Denial-of-service attack

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reinforcement learning-Based Fault-Tolerant Control of Uncertain Strict-Feedback Nonlinear Systems With Intermittent Actuator Faults

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2025年

作者： Yang, Qinmin Li, Huaying Ruan, Zhengwei Fan, Bo Sam Ge, Shuzhi Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Hangzhou Huawei Commun Technol Co Ltd Hangzhou 310027 Peoples R China Xi An Jiao Tong Univ Sch Automat Sci & Engn Xian 710049 Peoples R China Natl Univ Singapore Inst Funct Intelligent Mat Dept Elect & Comp Engn Singapore 117583 Singapore

In this work, a novel reinforcement learning-based adaptive fault-tolerant control (FTC) scheme with actuator redundancy is presented for a nonlinear strict-feedback system with nonlinear dynamics and uncertainties. A learning-based switching function technique is established to steer different groups of actuators automatically and successively to mitigate the impact of faulty actuators by observing a switching performance index. The optimal tracking control problem (OTCP) of strict-feedback nonlinear systems is transformed into an equivalent optimal regulation problem of each affine subsystem via adaptive feedforward controllers. Subsequently, the designed objective functions associated with Hamilton-Jacobi-Bellman (HJB) estimate errors caused by neural network (NN) approximations can be minimized by the reinforcement learning algorithm without value or policy iterations. It is proved that the tracking objective can be achieved and all signals in the closed-loop system can be guaranteed to be bounded, as long as the minimum time interval between two successive failures is bounded. Theoretical results are verified by simulations.

关键词： Actuators Switches Fault tolerant systems Control systems Optimal control Redundancy Nonlinear dynamical systems adaptive control Regulation Industrial control Approximate dynamic programming (ADP) fault-tolerant control (FTC) intermittent actuator faults reinforcement learning (RL) uncertain strict-feedback nonlinear systems

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Q-learning-Based adaptive Defense Mechanism for Connected Autonomous Vehicles

Q-Learning-Based Adaptive Defense Mechanism for Connected Au...

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ieee Southeastcon

作者： Izison Benibo Jagruti Sahoo Judith Mwakalonge Nana Kankam Gyimah Gurcan Comert Biswajit Biswal Nikunja Swain South Carolina State University Orangeburg South Carolina US North Carolina A&T University Greensboro North Carolina US

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

Connected Autonomous Vehicles (CAVs) are poised to operate alongside human-driven vehicles in mixed autonomy scenarios. However, their reliance on advanced software systems introduces significant cybersecurity vulnerabilities, including code injection and control system exploits, which pose risks to safety and reliability. While reinforcement learning (RL) has been extensively utilized to optimize decision-making in CAVs, its application to cybersecurity remains limited. In this paper, we propose an adaptive defense mechanism that leverages code diversification and allows dynamic switching of the CAV software from one code variant to another, where code variants refer to implementation of CAV software in multiple programming languages. The proposed mechanism is designed based on Q-learning, an RL approach to select the optimal code variant of the software to ensure resiliency. We design the reward function of our Q-learning agent in a ${way}$ that prioritizes less vulnerable states and penalizes the agent for unnecessary transitions. By incorporating the switching cost in the reward function, we ensure reduced operational cost while enhancing security. This paper demonstrates the potential of RL as a proactive cybersecurity strategy, fostering safer and more resilient autonomous transportation systems.

关键词： Codes Q-learning Costs Transportation Switches Software systems Software reliability Computer security Vehicle dynamics Autonomous vehicles

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Machine learning-Based Resource Allocation in 6G Integrated Space and Terrestrial Networks-Aided Intelligent Autonomous Transportation

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ieee Transactions on Intelligent Transportation Systems 2025年

作者： Prabhashana, Sasinda C. Huynh, Dang Van Singh, Keshav Zepernick, Hans-Jürgen Dobre, Octavia A. Shin, Hyundong Duong, Trung Q. Memorial University St. John’sNLA1C 5S7 Canada National Sun Yat-sen University Kaohsiung804 Taiwan Blekinge Institute of Technology Karlskrona37179 Sweden Kyung Hee University Korea Republic of Queen’s University Belfast BelfastBT7 1NN United Kingdom

The integration of terrestrial and non-terrestrial networks with mobile edge computing (MEC) and orbital edge computing (OEC) technologies is essential for advancing 6G communication networks. This paper introduces a network architecture that combines terrestrial and non-terrestrial networks by integrating drones (also known as UAV)-carried reconfigurable intelligent surfaces (RIS) and satellite-based MEC to optimize resource allocation in intelligent autonomous transportation systems (IATS). The primary objective is to minimize total system utility costs through the optimal allocation of bandwidth, computational power at the base station and low Earth orbit (LEO) satellite, and offloading decisions, all while adhering to strict performance and delay constraints. We address the complex resource optimization challenge by formulating a nonlinear programming (NLP) problem. To solve this problem, we employ long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) and LSTM-enhanced twin delayed deep deterministic policy gradient (TD3) algorithms, which enable dynamic and adaptive resource management. These LSTM-enhanced algorithms improve convergence speed by 44.44% and 73.81%, respectively, compared to their conventional counterparts, while significantly enhancing cost efficiency. Our simulation results demonstrate substantial improvements in system performance, with effective resource allocation and minimal utility costs, providing a robust solution for ensuring high-quality, low-latency communication in diverse 6G IATS environments. © 2000-2011 ieee.

关键词： Deep reinforcement learning

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PDP: Parallel dynamic programming

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ieee/CAA Journal of Automatica Sinica 2017年第1期4卷 1-5页

作者： Fei-Yue Wang Jie Zhang Qinglai Wei Xinhu Zheng Li Li IEEE State Key Laboratory of Management and Control for Complex Systems(SKL-MCCS) Institute of AutomationChinese Academy of Sciences(CASIA) School of Computer and Control Engineering University of Chinese Academy of Sciences Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences(SKL-MCCSCASIA) Qingdao Academy of Intelligent Industries Department of Computer Science and Engineering University of Minnesota Department of Automation Tsinghua University

Deep reinforcement learning is a focus research area in artificial intelligence. The principle of optimality in dynamic programming is a key to the success of reinforcement learning methods. The principle of adaptive dynamic programming ADP is first presented instead of direct dynamic programming DP , and the inherent relationship between ADP and deep reinforcement learning is developed. Next, analytics intelligence, as the necessary requirement, for the real reinforcement learning, is discussed. Finally, the principle of the parallel dynamic programming, which integrates dynamic programming and analytics intelligence, is presented as the future computational intelligence. © 2014 Chinese Association of Automation.

关键词： Artificial intelligence Neural networks reinforcement learning

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Exploring the Relationship of Reward and Punishment in reinforcement learning Evolving Action Meta-learning Functions in Goal Navigation

Exploring the Relationship of Reward and Punishment in Reinf...

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4th ieee International symposium on adaptive dynamic programming and reinforcement learning (ADPRL)

作者： Lowe, Robert Ziemke, Tom Univ Skovde Interact Lab Skovde Sweden

ISBN: (纸本)9781467359252

We present a reinforcement learning algorithm based on Dyna-Sarsa that utilizes separate representations of reward and punishment when guiding state-action value learning and action selection. The adoption of policy meta-learning optimized by a genetic algorithm is explored and results in the context of a two-armed bandit goal-navigation task in a simple grid world are presented. The findings argue for an important role for a genetic algorithm approach for constructing the foundations of autonomous reinforcement learning agents.

关键词： Value Reward Punishment reinforcement Contingencies SARSA TD learning Genetic Algorithm

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Special issue on adaptive dynamic programming and reinforcement learning in feedback control

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ieee TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2008年第4期38卷 896-897页

作者： Lewis, F. L. Liu, Derong Lendaris, George G. Univ Texas Arlington Dept Elect Engn Automat & Robot Res Inst Arlington TX 76019 USA Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA Portland State Univ Dept Elect & Comp Engn Syst Sci Grad Program Portland OR 97207 USA

The 18 papers in this special issue focus on adaptive dynamic programming and reinforcement learning in feedback control.

关键词： Programmable control adaptive control dynamic programming learning Feedback control Riccati equations Control systems Optimal control Neural networks Function approximation

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