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Mixed Motivation Driven Social Multi-Agent Reinforcement Learning for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1272-1282页

作者： Long Chen Peng Deng Lingxi Li Xuemin Hu State Key Laboratory of Multimodal Artificial Intelligence Systems and the State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing WAYTOUS Inc. Beijing Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Computer Science and Information Engineering Hubei University Wuhan China Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN USA School of Artificial Intelligence Hubei University Wuhan Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan China

Despite great achievement has been made in autonomous driving technologies, autonomous vehicles (AVs) still exhibit limitations in intelligence and lack social coordination, which is primarily attributed to their reliance on single-agent technologies, neglecting inter-AV interactions. Current research on multi-agent autonomous driving (MAAD) predominantly focuses on either distributed individual learning or centralized cooperative learning, ignoring the mixed-motive nature of MAAD systems, where each agent is not only self-interested in reaching its own destination but also needs to coordinate with other traffic participants to enhance efficiency and safety. Inspired by the mixed motivation of human driving behavior and their learning process, we propose a novel mixed motivation driven social multi-agent reinforcement learning method for autonomous driving. In our method, a multi-agent reinforcement learning (MARL) algorithm, called Social Learning Policy Optimization (SoLPO), which takes advantage of both the individual and social learning paradigms, is proposed to empower agents to rapidly acquire self-interested policies and effectively learn socially coordinated behavior. Based on the proposed SoLPO, we further develop a mixed-motive MARL method for autonomous driving combined with a social reward integration module that can model the mixed-motive nature of MAAD systems by integrating individual and neighbor rewards into a social learning objective for improved learning speed and effectiveness. Experiments conducted on the MetaDrive simulator show that our proposed method outperforms existing state-of-the-art MARL approaches in metrics including the success rate, safety, and efficiency. More-over, the AVs trained by our method form coordinated social norms and exhibit human-like driving behavior, demonstrating a high degree of social coordination.

关键词： Measurement Reinforcement learning Safety Driver behavior Autonomous vehicles Optimization

Frequency-splitting SWIPT with joint signal detection and nonlinear distortion compensation

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Frequency-splitting SWIPT with joint signal detection and no...

2021 IEEE International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2021

作者： Rajaram, Akashkumar Dinis, Rui Guerreiro, Joao Jayakody, Dushantha Nalin K. Beko, Marko NOVA School of Science and Technology NOVA University Lisbon Caparica Portugal School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk Russia Centre for Telecommunication Research School of Engineering Sri Lanka Technological Campus Padukka Sri Lanka Instituto de Telecomunicações Instituto Superior t'Ecnico Universidade de Lisboa Lisbon Portugal COPELABS Universidade Lusófona de Humanidades e Tecnologias Lisbon Portugal

ISBN: (纸本)9781665412629

Frequency splitting based simultaneous wireless information and power transmission (FS-SWIPT) is a novel SWIPT technique that can be used with orthogonal frequency division multiplexing (OFDM) or with other frequency division multiplexing techniques. In this work, we implement FS-SWIPT with OFDM signals and adopt separate subcarriers for data and energy carrying symbols. The main advantage of FS-SWIPT is that the data symbols do not suffer energy loss. However, the amplification of high power symbols can lead to nonlinear distortion and considerable performance degradation. To tackle the nonlinear distortion effects, we propose a joint signal detection and distortion compensation scheme. This compensation technique takes advantage of the known energy symbols and improves the performance comparatively with the case where energy symbols are not transmitted. We validate the analytical results of the nonlinear distortion effects and analyse the improvements in error rate performance of FS-SWIPT by using Monte Carlo simulations. © 2021 IEEE.

关键词： Intelligent systems

Technique Report of CVPR 2024 PBDL Challenges

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arXiv 2024年

作者： Lu, Xiaoqiang Jiao, Licheng Liu, Fang Liu, Xu Li, Lingling Ma, Wenping Yang, Shuyuan Xie, Haiyang Zhao, Jian Huang, Shihua Cheng, Peng Shen, Xi Wang, Zheng An, Shuai Zhu, Caizhi Li, Xuelong Chen, Linwei Fu, Ying Zhang, Tao Liu, Yu Yan, Chenggang Wang, Zichun Wang, Qinliang Liu, Yang Yu, Xinyue Jia, Sen Zhang, Junpei Chen, Puhua Chen, Xiang Li, Hao Pan, Jinshan Xie, Chuanlong Chen, Hongming Li, Mingrui Deng, Tianchen Huang, Jingwei Li, Yufeng Wan, Fei Xu, Bingxin Cheng, Jian Liu, Hongzhe Xu, Cheng Zou, Yuxiang Pan, Weiguo Dai, Songyin Jiang, Linyan Song, Bingyi An, Zhuoyu Lei, Haibo Luo, Qing Song, Jie Liu, Yuan Li, Qihang Zhang, Haoyuan Wang, Lingfeng Chen, Wei Luo, Aling Li, Cheng Cao, Jun Chen, Shu Dou, Zifei Liu, Xinyu Zhang, Jing Zhang, Kexin Yang, Yuting Zhang, Liwen Xu, Zhe Gou, Dingyong Li, Cong Xu, Senyan Zhang, Yunkang Jiang, Siyuan Liu, Qinglin Yu, Wei Lv, Xiaoqian Li, Jianing Zhang, Shengping Ji, Xiangyang Zou, Yunhao Chen, Yuanpei Zhang, Yuhan Peng, Weihang Gou, Xuejian Zhao, Shizhan Zhang, Yanzhao Yan, Libo Guo, Yuwei Li, Guoxin Gao, Qiong Che, Chenyue Sun, Long School of Artificial Intelligence Xidian University China School of Computer Science Wuhan University China Intellindust China Beijing Forestry University China China Telecom China Harbin Institute of Technology China Northwestern Polytechnical University China China Telecom China Beijing Institute of Technology China Lishui Institute of Hangzhou Dianzi University China Tsinghua University China Intelligent Perception and Image Understanding Lab Xidian University China Nanjing University of Science and Technology China Shenyang Aerospace University China Dalian University of Technology China Shanghai Jiao Tong University China University of Electronic Science and Technology of China China Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing100101 China College of Robotics Beijing Union University China Tencent China Sanechips Technology Co. LTD. China Xiaomi Inc. China ZTE Corporation China University of Science and Technology of China China Peking University China Intelligent Science & Technology Academy of CASIC China

The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to inform and enhance deep learning models, we can develop more robust and accurate vision systems. Physics-based vision aims to invert the processes to recover scene properties such as shape, reflectance, light distribution, and medium properties from images. In recent years, deep learning has shown promising improvements for various vision tasks, and when combined with physics-based vision, these approaches can enhance the robustness and accuracy of vision systems. This technical report summarizes the outcomes of the Physics-Based Vision Meets Deep Learning (PBDL) 2024 challenge, held in CVPR 2024 workshop. The challenge consisted of eight tracks, focusing on Low-Light Enhancement and Detection as well as High Dynamic Range (HDR) Imaging. This report details the objectives, methodologies, and results of each track, highlighting the top-performing solutions and their innovative approaches. © 2024, CC BY.

关键词： Machine vision

Normal Distribution Function on Descriptor Extraction 11th

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Normal Distribution Function on Descriptor Extraction

11th International Conference on Image and Graphics, ICIG 2021

作者： Yin, Jianhua Liu, Cong Jiang, Jun Wen, Jie Yang, Lihang Zhu, Shilong School of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China College of Big Data and Information Engineering Guizhou University Guizhou Guiyang550025 China

ISBN: (纸本)9783030873578

The triplet-loss function is widely used in fields of descriptor extraction in recent years and owing to its good performance in various databases. However, some recent works make less effort on the relationship of adjacent descriptors from the same sample, which leads to the instability of descriptors and results in the mismatching problem in practical applications. To solve this problem, we introduce the topological relationship with the Normal Distribution Function (NDF) into the triplet loss function. The loss function establishes the relationship of descriptors from the same sample and considers the interrelation of descriptors from different types of samples. Furthermore, to increase the calculation speed, we normalize the algorithm NDF. Finally, we propose the triplet-loss function on three databases. These results demonstrate that our algorithm obtains better performance than state-of-the-art methods. © 2021, Springer Nature Switzerland AG.

关键词： Distribution functions

Dealing with non-stationarity in multi-agent reinforcement learning via trust region decomposition

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arXiv 2021年

作者： Li, Wenhao Wang, Xiangfeng Jin, Bo Sheng, Junjie Zha, Hongyuan School of Computer Science and Technology East China Normal University Shanghai China School of Computer Science and Technology East China Normal University Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Non-stationarity is one thorny issue in multi-agent reinforcement learning, which is caused by the policy changes of agents during the learning procedure. Current works to solve this problem have their own limitations in effectiveness and scalability, such as centralized critic and decentralized actor (CCDA), population-based self-play, modeling of others and etc. In this paper, we novelly introduce a δ-stationarity measurement to explicitly model the stationarity of a policy sequence, which is theoretically proved to be proportional to the joint policy divergence. However, simple policy factorization like mean-field approximation will mislead to larger policy divergence, which can be considered as trust region decomposition dilemma. We model the joint policy as a general Markov random field and propose a trust region decomposition network based on message passing to estimate the joint policy divergence more accurately. The Multi-Agent Mirror descent policy algorithm with Trust region decomposition, called MAMT, is established with the purpose to satisfy δ-stationarity. MAMT can adjust the trust region of the local policies adaptively in an end-to-end manner, thereby approximately constraining the divergence of joint policy to alleviate the non-stationary problem. Our method can bring noticeable and stable performance improvement compared with baselines in coordination tasks of different complexity. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

Insurance Contract for High Renewable Energy Integration

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arXiv 2022年

作者： Zhao, Dongwei Wang, Hao Huang, Jianwei Lin, Xiaojun The MIT Energy Initiative Massachusetts Institute of Technology United States The Department of Data Science and Artificial Intelligence Monash University Australia The School of Science and Engineering The Chinese University of Hong Kong Shenzhen and Shenzhen Institute of Artificial Intelligence and Robotics for Society China The Elmore Family School of Electrical and Computer Engineering Purdue University United States

The increasing penetration of renewable energy poses significant challenges to power grid reliability. There have been increasing interests in utilizing financial tools, such as insurance, to help end-users hedge the potential risk of lost load due to renewable energy variability. With insurance, a user pays a premium fee to the utility, so that he will get compensated in case his demand is not fully satisfied. A proper insurance design needs to resolve the following two challenges: (i) users’ reliability preference is private information;and (ii) the insurance design is tightly coupled with the renewable energy investment decision. To address these challenges, we adopt the contract theory to elicit users’ private reliability preferences, and we study how the utility can jointly optimize the insurance contract and the planning of renewable energy. A key analytical challenge is that the joint optimization of the insurance design and the planning of renewables is non-convex. We resolve this difficulty by revealing important structural properties of the optimal solution, using the help of two benchmark problems: the no-insurance benchmark and the social-optimum benchmark. Compared with the no-insurance benchmark, we prove that the social cost and users’ total energy cost are always no larger under the optimal contract. Simulation results show that the largest benefit of the insurance contract is achieved at a medium electricity-bill price together with a low type heterogeneity and a high renewable uncertainty. © 2022, CC BY.

关键词： Insurance

Learned descriptor using dynamical exponential algorithm 2

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Learned descriptor using dynamical exponential algorithm

2nd International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2021

作者： Yin, Jianhua Liu, Cong Wen, Jie Chen, Junhong Jiang, Jun Liu, Hui Zhu, Longzhen School of Computer Science and Technology Harbin Institute of Technology Shenzhen Shenzhen518055 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China Hengfeng Bank Co. Ltd. Shanghai200000 China

ISBN: (纸本)9783800756162

Recent works improve the performance of learned descriptors by the triplet loss function and make some effort in finding the hard negative samples. However, these methods pay less attention to the weight parameter of the loss function to improve the stability of learned descriptors. Unstable descriptors can cause mismatching. To solve the problem, we design a dynamical exponential algorithm (DEA) and introduce the algorithm into the triplet loss function. This algorithm can make that the distance between nonmatching descriptors is exceeded to the distance between matching descriptors. It can improve the stability of the learned descriptor by increasing the difference between two distances. We demonstrate that the performance of the consistent descriptors reached state-of-the-art on various datasets. © 2021 AIIPCC 2021 - 2nd International Conference on Artificial Intelligence, Information Processing and Cloud Computing. All rights reserved.

关键词： Artificial intelligence

Unsupervised Cross-Domain 3D Human Pose Estimation via Pseudo-Label-Guided Global Transforms

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arXiv 2025年

作者： Liu, Jingjing Wang, Zhiyong Fan, Xinyu Dadashzadeh, Amirhossein Liu, Honghai Mirmehdi, Majid School of Computer Science University of Bristol United Kingdom State Key Laboratory of Robotics and Systems Harbin Institute of Technology Shenzhen China School of Aerospace Engineering Xiamen University China

Existing 3D human pose estimation methods often suffer in performance, when applied to cross-scenario inference, due to domain shifts in characteristics such as camera viewpoint, position, posture, and body size. Among these factors, camera viewpoints and locations have been shown to contribute significantly to the domain gap by influencing the global positions of human poses. To address this, we propose a novel framework that explicitly conducts global transformations between pose positions in the camera coordinate systems of source and target domains. We start with a Pseudo-Label Generation Module that is applied to the 2D poses of the target dataset to generate pseudo-3D poses. Then, a Global Transformation Module leverages a human-centered coordinate system as a novel bridging mechanism to seamlessly align the positional orientations of poses across disparate domains, ensuring consistent spatial referencing. To further enhance generalization, a Pose Augmentor is incorporated to address variations in human posture and body size. This process is iterative, allowing refined pseudo-labels to progressively improve guidance for domain adaptation. Our method is evaluated on various cross-dataset benchmarks, including Human3.6M, MPI-INF-3DHP, and 3DPW. The proposed method outperforms state-of-the-art approaches and even outperforms the target-trained model. Copyright © 2025, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

Feature Selection for Precise Anomaly Detection in Substation Automation Systems

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Feature Selection for Precise Anomaly Detection in Substatio...

IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Xuelei Wang Colin Fidge Ghavameddin Nourbakhsh Ernest Foo Zahra Jadidi Calvin Li School of Computer Science Queensland University of Technology Brisbane Australia School of Electrical Engineering and Robotics Queensland University of Technology Brisbane Australia School of Information and Communication Technology Griffith University Brisbane Australia Asset & Operations Jemena Sydney Australia

ISBN: (纸本)9781665448796

With the rapid advancement of the electrical grid, substation automation systems (SASs) have been developing continuously. However, with the introduction of advanced features, such as remote control, potential cyber security threats in SASs are also increased. Additionally, crucial components in SASs, such as protection relays, usually come from third-party vendors and may not be fully trusted. Untrusted devices may stealthily perform harmful or unauthorised behaviours which could compromise or damage SASs, and therefore, bring adverse impacts to the primary plant. Thus, it is necessary to detect abnormal behaviours from an untrusted device before it brings about catastrophic impacts. Anomaly detection techniques are suitable to detect anomalies in SASs as they only bring minimal side-effects to normal system operations. Many researchers have developed various machine learning algorithms and mathematical models to improve the accuracy of anomaly detection. However, without prudent feature selection, it is difficult to achieve high accuracy when detecting attacks launched from internal trusted networks, especially for stealthy message modification attacks which only modify message payloads slightly and imitate patterns of benign behaviours. Therefore, this paper presents choices of features which improve the accuracy of anomaly detection within SASs, especially for detecting “stealthy” attacks. By including two additional features, Boolean control data from message payloads and physical values from sensors, our method improved the accuracy of anomaly detection by decreasing the false-negative rate from 25% to 5% approximately.

关键词： Performance evaluation Substation automation Systems operation Sensor phenomena and characterization Feature extraction Mathematical models Relays