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Optimal synchronization control for multi-agent systems with input saturation: a nonzero-sum game Research Article

Hongyang LI, Qinglai WEI

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1010-1019 doi: 10.1631/FITEE.2200010

Abstract: This paper presents a novel method for with . The multi-agent game theory is introduced to transform the problem into a multi-agent . Then, the Nash equilibrium can be achieved by solving the coupled Hamilton–Jacobi–Bellman (HJB) equations with nonquadratic input energy terms. A novel method is presented to obtain the Nash equilibrium solution without the system models, and the critic neural networks (NNs) and actor NNs are introduced to implement the presented method. Theoretical analysis is provided, which shows that the iterative control laws converge to the Nash equilibrium. Simulation results show the good performance of the presented method.

Keywords： synchronization control     Multi-agent systems     Nonzero-sum game     Adaptive dynamic programming     Input saturation     Off-policyreinforcement learning     Policy iteration    

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-022-0673-7

Abstract: CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learningFirst, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controllingSecond, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-termACNN is also compared with other published machine learning (ML) and deep learning (DL) methods.

Keywords： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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Automated synthesis of steady-state continuous processes using reinforcement learning

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 288-302 doi: 10.1007/s11705-021-2055-9

Abstract: The present work demonstrates how reinforcement learning can be used for automated flowsheet synthesis

Keywords： automated process synthesis     flowsheet synthesis     artificial intelligence     machine learning     reinforcementlearning    

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A home energy management approach using decoupling value and policy in reinforcement learning

熊珞琳,唐漾,刘臣胜,毛帅,孟科,董朝阳,钱锋

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9,   Pages 1261-1272 doi: 10.1631/FITEE.2200667

Abstract: In this paper, a novel home energy management (HEM) approach is proposed based on a data-driven deep reinforcementlearning method.enhance the energy optimization performance by alleviating the overfitting problem caused by the shared policyFurthermore, separate networks for the policy and value functions ensure the generalization of the proposed

Keywords： Home energy system     Electric vehicle     Reinforcement learning     Generalization    

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Deep reinforcement learning-based critical element identification and demolition planning of frame structures

Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 11,   Pages 1397-1414 doi: 10.1007/s11709-022-0860-y

Abstract: quantitative indices considering the severity of the ultimate collapse scenario are proposed using reinforcementlearning and graph embedding.By establishing the policy network, the agent outputs the Q value for each action after observinglearning model is trained offline.Besides, it is proved that the Q values produced by the reinforcement learning agent can make

Keywords： progressive collapse     alternate load path     demolition planning     reinforcement learning     graph embedding    

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Toward Trustworthy Decision-Making for Autonomous Vehicles: A Robust Reinforcement Learning Approach

Xiangkun He,Wenhui Huang,Chen Lv,

Engineering doi: 10.1016/j.eng.2023.10.005

Abstract: Therefore, we present a novel robust reinforcement learning approach with safety guarantees to attainThe proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety

Keywords： Autonomous vehicle     Decision-making     Reinforcement learning     Adversarial attack     Safety guarantee    

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Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse ReinforcementLearning Theory

Jian Wu,Yang Yan,Yulong Liu,Yahui Liu,

Engineering doi: 10.1016/j.eng.2023.07.018

Abstract: , a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcementlearning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition

Keywords： Obstacle avoidance trajectory planning     Inverse reinforcement theory     Anthropomorphic     Adaptive driving    

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Recent development on statistical methods for personalized medicine discovery

Yingqi Zhao, Donglin Zeng

Frontiers of Medicine 2013, Volume 7, Issue 1,   Pages 102-110 doi: 10.1007/s11684-013-0245-7

Abstract:

It is well documented that patients can show significant heterogeneous responses to treatments so the best treatment strategies may require adaptation over individuals and time. Recently, a number of new statistical methods have been developed to tackle the important problem of estimating personalized treatment rules using single-stage or multiple-stage clinical data. In this paper, we provide an overview of these methods and list a number of challenges.

Keywords： dynamic treatment regimes     personalized medicine     reinforcement learning     Q-learning    

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Minimax Q-learning design for H∞ control of linear discrete-time systems Research Articles

Xinxing LI, Lele XI, Wenzhong ZHA, Zhihong PENG,lixinxing_1006@163.com,xilele.bit@gmail.com,zhawenzhong@126.com,peng@bit.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3,   Pages 438-451 doi: 10.1631/FITEE.2000446

Abstract: The proposed method, which employs off-policy , learns the optimal control policies for the controllerDifferent from existing -learning methods, a novel gradient-based policy improvement scheme is proposedconverges to the saddle solution under initially admissible control policies and an appropriate positive learning

Keywords： H∞ control     Zero-sum dynamic game     Reinforcement learning     Adaptive dynamic programming     Minimax Q-learning     Policy iteration    

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Actor–Critic Reinforcement Learning and Application in Developing Computer-Vision-Based Interface Tracking Article

Oguzhan Dogru, Kirubakaran Velswamy, Biao Huang

Engineering 2021, Volume 7, Issue 9,   Pages 1248-1261 doi: 10.1016/j.eng.2021.04.027

Abstract: A reinforcement learning (RL) agent successfully tracks an interface between two liquids, which is oftenUnlike supervised learning (SL) methods that rely on a huge number of parameters, this approach requirespaper provides a detailed review of one of the most effective RL methodologies: actor–critic policy

Keywords： Interface tracking     Object tracking     Occlusion     Reinforcement learning     Uniform manifold approximation    

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Motion planning of a quadrotor robot game using a simulation-based projected policy iteration method Regular Papers

Li-dong ZHANG, Ban WANG, Zhi-xiang LIU, You-min ZHANG, Jian-liang AI

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 4,   Pages 525-537 doi: 10.1631/FITEE.1800571

Abstract: Recently, the state-of-the-art algorithms in reinforcement learning studies have been developed, providingproblem in the threedimensional space and an approximate dynamic programming approach using a projected policyiteration method for learning the utilities of game states and improving motion policies.

Keywords： Reinforcement learning     Approximate dynamic programming     Decision making     Motion planning     Unmanned aerial    

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Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning Research Article

Shihmin WANG, Binqi ZHAO, Zhengfeng ZHANG, Junping ZHANG, Jian PU

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1541-1556 doi: 10.1631/FITEE.2300084

Abstract: exploration methods that sample an action from different types of posterior distributions, we focus on the policySpecifically, we first employ in the policy to generate an action candidate set.Then we introduce a clustering buffer for modeling the internal hierarchy, which consists of on-policydata, off-policy data, and expert data to evaluate actions from the clusters in the action candidate

Keywords： Reinforcement learning     Sample efficiency     Sampling process     Clustering methods     Autonomous driving    

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Toward Human-in-the-loop AI: Enhancing Deep Reinforcement Learning Via Real-time Human Guidance for Autonomous Article

Jingda Wu, Zhiyu Huang, Zhongxu Hu, Chen Lv

Engineering 2023, Volume 21, Issue 2,   Pages 75-91 doi: 10.1016/j.eng.2022.05.017

Abstract:

Due to its limited intelligence and abilities, machine learning is currentlytraining loop of artificial intelligence (AI), leveraging human intelligence to further advance machine learningIn this study, a real-time human-guidance-based (Hug)-deep reinforcement learning (DRL) method is developedfor policy training in an end-to-end autonomous driving case.Based on this human-in-the-loop guidance mechanism, an improved actor-critic architecture with modified policy

Keywords： Human-in-the-loop AI     Deep reinforcement learning     Human guidance     Autonomous driving    

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Advanced purification and comprehensive utilization of yellow phosphorous off gas

Ping NING,Xiangyu WANG

Frontiers of Environmental Science & Engineering 2015, Volume 9, Issue 2,   Pages 181-189 doi: 10.1007/s11783-014-0698-1

Abstract: Without appropriate purification and removal, this off gas has potential to cause severe pollution problemsPurified yellow phosphorous off gas can be beneficially reused as a raw material in chemical productionIn this paper, the significance of purification and reutilization of yellow phosphorous off gas are exploredprocesses, and main characteristics of the technologies for purification and reuse of yellow phosphorus offcomprehensive reutilization can be an effective solution for heavy pollution resulting from yellow phosphorous off

Keywords： yellow phosphorous off gas     purification     comprehensive utilization    

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Coach-assisted multi-agent reinforcement learning framework for unexpected crashed agents Research Article

Jian ZHAO, Youpeng ZHAO, Weixun WANG, Mingyu YANG, Xunhan HU, Wengang ZHOU, Jianye HAO, Houqiang LI

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1032-1042 doi: 10.1631/FITEE.2100594

Abstract: We have designed three coaching strategies (fixed crash rate, curriculum learning, and adaptive crashdemonstrate the efficacy of the adaptive strategy compared with the fixed crash rate strategy and curriculum learning

Keywords： Multi-agent system     Reinforcement learning     Unexpected crashed agents    

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Title Author Date Type Operation

Optimal synchronization control for multi-agent systems with input saturation: a nonzero-sum game

Hongyang LI, Qinglai WEI

Journal Article

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Journal Article

Automated synthesis of steady-state continuous processes using reinforcement learning

Journal Article

A home energy management approach using decoupling value and policy in reinforcement learning

熊珞琳,唐漾,刘臣胜,毛帅,孟科,董朝阳,钱锋

Journal Article

Deep reinforcement learning-based critical element identification and demolition planning of frame structures

Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO

Journal Article

Toward Trustworthy Decision-Making for Autonomous Vehicles: A Robust Reinforcement Learning Approach

Xiangkun He,Wenhui Huang,Chen Lv,

Journal Article

Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse ReinforcementLearning Theory

Jian Wu,Yang Yan,Yulong Liu,Yahui Liu,

Journal Article

Recent development on statistical methods for personalized medicine discovery

Yingqi Zhao, Donglin Zeng

Journal Article

Minimax Q-learning design for H∞ control of linear discrete-time systems

Xinxing LI, Lele XI, Wenzhong ZHA, Zhihong PENG,lixinxing_1006@163.com,xilele.bit@gmail.com,zhawenzhong@126.com,peng@bit.edu.cn

Journal Article

Actor–Critic Reinforcement Learning and Application in Developing Computer-Vision-Based Interface Tracking

Oguzhan Dogru, Kirubakaran Velswamy, Biao Huang

Journal Article

Motion planning of a quadrotor robot game using a simulation-based projected policy iteration method

Li-dong ZHANG, Ban WANG, Zhi-xiang LIU, You-min ZHANG, Jian-liang AI

Journal Article

Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning

Shihmin WANG, Binqi ZHAO, Zhengfeng ZHANG, Junping ZHANG, Jian PU

Journal Article

Toward Human-in-the-loop AI: Enhancing Deep Reinforcement Learning Via Real-time Human Guidance for Autonomous

Jingda Wu, Zhiyu Huang, Zhongxu Hu, Chen Lv

Journal Article

Advanced purification and comprehensive utilization of yellow phosphorous off gas

Ping NING,Xiangyu WANG

Journal Article

Coach-assisted multi-agent reinforcement learning framework for unexpected crashed agents

Jian ZHAO, Youpeng ZHAO, Weixun WANG, Mingyu YANG, Xunhan HU, Wengang ZHOU, Jianye HAO, Houqiang LI

Journal Article