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Generation a shooting on the walking for soccer simulation 3D league using q-learning algorithm

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第6期14卷 6947-6957页

作者： Li, Yun Song, Yibin Rezaeipanah, Amin Yantai Inst Technol Sch Informat Engn Yantai 264005 Shandong Peoples R China Univ Rahjuyan Danesh Borazjan Dept Comp Engn Bandar Bushehr Iran

RoboCup is one of the greatest human endeavors to operationalize research in the field of robotics and artificial intelligence. RoboCup soccer simulation 3D (RoboCup3D) competition provide a great opportunity to work with humanoid robots without the need for hardware. One of the most important goals of the teams participating in RoboCup3D league is the ability to increase the number of shoots. The reason for this importance is that superiority over the opponent requires a powerful and precise shoot. The methods introduced for shooting so far are mostly based on Inverse Kinematics (IK) and point analysis. The assumption of these methods is that the positions of the robot and the ball is fixed. However, this is not always the case for shooting. In this paper, a shooting strategy is presented for situations where the robot is walking. Here, a curved path is designed to movement the robot towards the ball so that the robot will eventually have an optimal position to shoot. In general, the vision preceptor in RoboCup3D has noise. Hence, robot movement parameters such as speed and angle are more precisely adjusted by the q-learning algorithm. Finally, when the robot is in the optimal position relative to the ball and the goal, the IK module is applied to the shooting strategy. Simulations and experiments prove the superiority of the proposed algorithm compared to most teams in RoboCupSoccer and Iran's Open RoboCup3D leagues.

关键词： RoboCup3D Soccer simulation 3D league Shooting strategy q-learning algorithm

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Disassembly sequence planning for target parts of end-of-life smartphones using q-learning algorithm

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Procedia CIRP 2023年 116卷 684-689页

作者： Zepeng Chen Lin Li Fu Zhao John W. Sutherland Fengfu Yin College of Electromechanical Engineering Qingdao University of Science and Technology Qingdao 266061 China School of Environmental and Ecological Engineering Purdue University West Lafayette IN 47907 USA School of Mechanical Engineering Purdue University West Lafayette IN 47907 USA

Owing to increasing environmental concerns, disassembly has become an important step in value recovery from end-of-life (EoL) products. As one of the prevalent electronic devices, smartphones are especially challenging to manage at the end of life. They have a diverse internal structure that leads to difficulties in modeling and developing general plans for disassembly. Given this background, an improved method that uses a q-learning algorithm is proposed to optimize the disassembly sequence of EoL smartphones. A constraint relationship is first developed of EoL smartphone parts. The disassembly sequence planning problem is then formalized with a Markov Decision Process. The optimization objective is the disassembly time to obtain the target parts. The disassembly time and the target parts are converted into a reward value for the q-learning algorithm. A State-Action-Reward Matrix is established based on converted reward value and disassembly sequence planning problem. A q-table is trained to find the best action for each state using the State-Action-Reward Matrix. The disassembly sequence with the maximum reward value is secured through the trained q-table and objective function. A case study of the ‘Xiaomi 5’ is conducted to verify the applicability of the proposed method. The experimental results demonstrate that the proposed method can provide feasible sequence planning for targeted parts in the disassembly of EoL smartphones.

关键词： EoL smartphones Disassembly sequence q-learning algorithm Disassembly time Target parts

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Research on heating emergency repair path planning based on AHP and improved q-learning algorithm

Research on heating emergency repair path planning based on ...

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第35届中国过程控制会议

作者： Xi Zhao Yilin Han Jiahua Zhu Yunsheng Fan Dingxiao Zhu College of Marine Electrical Engineering Dalian Maritime University Dalian Thermal Power New Energy Application Technology Research Institute Co..Ltd.

Aiming at the problems of low learning efficiency and slow convergence speed of traditional q-learning method in path planning, this paper proposes an improved q-learning algorithm for heating emergency repair path planning. To solve this problem, the algorithm initializes the q-table according to the heuristic function to accelerate the learning process, leading the agent to a better strategy in the early stage of the training process;Dynamic adjustment of greedy factor is added to balance the relationship between exploration and utilization. The Tabu Search algorithm is integrated to avoid falling into local path optimization, reduce the repetition rate of topology map exploration, and accelerate the convergence speed;Finally, based on the analytic hierarchy process, the urgency of emergency repair of heating failure is graded, and the visiting order of emergency repair failure points is planned according to the emergency degree, which is helpful to improve the efficiency of emergency repair. The experimental results show that the efficiency of the improved algorithm is better than that of the traditional algorithm in the path length and the number of path nodes, and it can converge better in the training process.

关键词： Route planning q-learning algorithm Tabu Search algorithm Analytic hierarchy process Emergency repair Heating system

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Divide and Conquer q-learning (DCqL) algorithm based Photovoltaic (PV) array reconfiguration scheme for alleviating the partial shading influence

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SOLAR ENERGY 2023年 249卷 21-39页

作者： Gao, Xin Deng, Fang Wu, Guoqiang Pan, qiufeng Zheng, Chengyu Wang, Wei Cai, Tianwang Jiang, Liyuan China Aerosp Sci & Technol Corp Acad Unmanned Syst Ctr 9 Beijing 100094 Peoples R China Aerosp Times FeiHong Technol Co Ltd Beijing 100094 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

Nonuniform irradiance on the Photovoltaic (PV) array would cause mismatch problem and decrease the generated power. To relieve the negative effects of partial shading, reconfiguring the interconnection of the PV array is a favorable solution. However, the current reconfiguration techniques are usually limited by different corresponding factors, e.g., not suit for various instantaneous shade cases, the shortage of experts' knowledge or large amounts of data, easy coverage to local optima, and the strict assumptions for application. Therefore, in this paper a unsupervised adaptive PV array reconfiguration technique is proposed with a Divide and Conquer q-learning (DCqL) based array reconfiguration scheme designed to decrease the influence of partial shades. To testify the performance of the proposed PV array reconfiguration scheme, mathematic theoretical evaluation and electric characteristic analyses are made and compared with those obtained by the conventional Total -Cross-Tied (TCT) arrangement, SuDoKu connection, Genetic algorithm (GA) based scheme, as well as some lately proposed Grasshopper Optimization algorithm (GOA), Harris Hawks Optimizer (HHO), and Lo Shu (LS) technique based reconfiguration methods. Besides, additional estimation indices, including mismatch loss, fill factor, percentage power loss and percentage power enhancement are calculated with substantial analyses and comparisons. Subsequently, the application of the proposed scheme in real-time conditions is evaluated, with energy saving and income analysis estimated in comparison with other eminent challenger reconfiguration schemes. At last, a comprehensive qualitative comparison assessment, with respect to dispersion ability under partial shading conditions (PSC), sensor requirement, wiring complexity and capital investment is carried out and proves the versatility of proposed methodology.

关键词： Photovoltaic array reconfiguration Reinforcement learning q-learning algorithm Divide and conquer principle Partial shadings

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An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using q-learning algorithm and Speech Recognition

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SENSORS 2022年第3期22卷 941-941页

作者： Kiangala, Kahiomba Sonia Wang, Zenghui Univ South Africa Coll Sci Engn & Technol CSET ZA-1710 Johannesburg South Africa Univ South Africa Dept Elect & Min Engn ZA-1710 Johannesburg South Africa

The industrial manufacturing sector is undergoing a tremendous revolution moving from traditional production processes to intelligent techniques. Under this revolution, known as Industry 4.0 (I40), a robot is no longer static equipment but an active workforce to the factory production alongside human operators. Safety becomes crucial for humans and robots to ensure a smooth production run in such environments. The loss of operating moving robots in plant evacuation can be avoided with the adequate safety induction for them. Operators are subject to frequent safety inductions to react in emergencies, but very little is done for robots. Our research proposes an experimental safety response mechanism for a small manufacturing plant, through which an autonomous robot learns the obstacle-free trajectory to the closest safety exit in emergencies. We implement a reinforcement learning (RL) algorithm, q-learning, to enable the path learning abilities of the robot. After obtaining the robot optimal path selection options with q-learning, we code the outcome as a rule-based system for the safety response. We also program a speech recognition system for operators to react timeously, with a voice command, to an emergency that requires stopping all plant activities even when they are far away from the emergency stops (ESTOPs) button. An ESTOP or a voice command sent directly to the factory central controller can give the factory an emergency signal. We tested this functionality on real hardware from an S7-1200 Siemens programmable logic controller (PLC). We simulate a simple and small manufacturing environment overview to test our safety procedure. Our results show that the safety response mechanism successfully generates paths without obstacles to the closest safety exits from all the factory locations. Our research benefits any manufacturing SME intending to implement the initial and primary use of autonomous moving robots (AMR) in their factories. It also impacts manufacturin

关键词： autonomous moving robot obstacle-free path planning q-learning algorithm reinforcement learning (RL) safety response smart manufacturing speech recognition

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Horizontal trajectory control of stratospheric airships in wind field using q-learning algorithm

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AEROSPACE SCIENCE AND TECHNOLOGY 2020年 106卷 106100-106100页

作者： Yang, Xiaowei Yang, Xixiang Deng, Xiaolong Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China

This paper proposes an adaptive horizontal trajectory control method for stratospheric airships in uncertain wind field using q-learning algorithm. Firstly, horizontal trajectory control of the airships is decomposed into the target tracking, and the observation model of airships is constructed. Then, the Markov decision process (MDP) model of airships is established, in which the action strategy is determined by the wind direction, and a cerebellar model articulation controller (CMAC) neural network is designed to optimize the action strategy for each state. Finally, numerical simulations demonstrate that the proposed control method performs well stability and intelligent decision-making ability in the process of horizontal trajectory control for stratospheric airships. (C) 2020 Elsevier Masson SAS. All rights reserved.

关键词： Stratospheric airships Horizontal trajectory control q-learning algorithm Wind field CMAC neural network

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A novel dynamic integration approach for multiple load forecasts based on q-learning algorithm

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2020年第7期30卷 1-14页

作者： Ma, Minhua Jin, Bingjie Luo, Shuxin Guo, Shaoqing Huang, Hongwei CSG Guangdong Power Grid Corp Grid Planning & Res Ctr Guangzhou Guangdong Peoples R China Guangdong Power Grid Dev Res Inst Co Ltd Guangzhou Guangdong Peoples R China Beijing QU Creat Technol Co Ltd Beijing Peoples R China

A higher accurate load forecasts can significantly reduce the potential cost for power system operation and power market decision. There have been extensive load forecasting methods proposed by now. Ensemble learning is an effective way to integrate various forecasts and further improve the forecasting accuracy. Traditional ensemble learning algorithms static. However, how to select a suitable forecasting method from a large number of load forecasting methods under the complex and varying environment conditions is still challenging. In this context, this paper proposes a dynamic integration approach for multiple load forecasts based on reinforcement learning. Compared with the traditional integration methods, this method adopts q-learning algorithm to realize the adaptive selection of the forecasting methods and dynamic weight calculation for different and varying environment. The basic principles and implementation processes of q-learning algorithm is firstly introduced. Then, the implementation methodology of dynamic integration approach of multiple load forecasts is proposed. On this basis, the environment state, action space and return function are designed according to the requirements of q-learning algorithm. Finally, a case study based on the real-world data in Guangzhou shows that the average forecasting accuracy could be improved by almost 3% by adopting the dynamic integration approach with the traditional ones.

关键词： action space environmental status multiple load forecasts integration q-learning algorithm reinforcement learning return function

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Coherent beam combination based on q-learning algorithm

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OPTICS COMMUNICATIONS 2021年 490卷 126930-126930页

作者： Zhang, Xi Li, Pingxue Zhu, Yunchen Li, Chunyong Yao, Chuanfei Wang, Luo Dong, Xueyan Li, Shun Beijing Univ Technol Fac Mat & Mfg Inst Ultrashort Pulsed Laser & Applicat Beijing 10024 Peoples R China Univ Durham Dept Phys South Rd Durham DH1 3LE England

Coherent beam combination (CBC) is an effective method to break the limiting power of a single fiber laser. The q-learning algorithm is one of the reinforcement learning algorithms. We use the q-learning algorithm to do phase compensation in the field of CBC. The performance difference between the q-learning algorithm and the stochastic parallel gradient descent optimization algorithm (SPGD) is analyzed by simulating time-domain coherent synthesis. The results show that the q-learning algorithm is easier to debug and has better stability.

关键词： Coherent beam combination q-learning algorithm Stochastic parallel gradient descent optimization algorithm

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Intelligent Control/Operational Strategies in WWTPs through an Integrated q-learning algorithm with ASM2d-Guided Reward

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WATER 2019年第5期11卷 927页

作者： Pang, Jiwei Yang, Shanshan He, Lei Chen, Yidi Ren, Nanqi Harbin Inst Technol State Key Lab Urban Water Resource & Environm Harbin 150000 Heilongjiang Peoples R China

The operation of a wastewater treatment plant (WWTP) is a typical complex control problem, with nonlinear dynamics and coupling effects among the variables, which renders the implementation of real-time optimal control an enormous challenge. In this study, a q-learning algorithm with activated sludge model No. 2d-guided (ASM2d-guided) reward setting (an integrated ASM2d-qL algorithm) is proposed, and the widely applied anaerobic-anoxic-oxic (AAO) system is chosen as the research paradigm. The integrated ASM2d-qL algorithms equipped with a self-learning mechanism are derived for optimizing the control strategies (hydraulic retention time (HRT) and internal recycling ratio (IRR)) of the AAO system. To optimize the control strategies of the AAO system under varying influent loads, q matrixes were built for both HRTs and IRR optimization through the pair of based on the integrated ASM2d-qL algorithm. 8 days of actual influent qualities of a certain municipal AAO wastewater treatment plant in June were arbitrarily chosen as the influent concentrations for model verification. Good agreement between the values of the model simulations and experimental results indicated that this proposed integrated ASM2d-qL algorithm performed properly and successfully realized intelligent modeling and stable optimal control strategies under fluctuating influent loads during wastewater treatment.

关键词： machine learning q-learning algorithm optimized control strategies activated sludge model No 2d (ASM2d) enhanced nutrients removal integrated ASM-qL algorithm

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Safeguarding multiuser communication using full-duplex jamming and q-learning algorithm

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IET COMMUNICATIONS 2018年第15期12卷 1805-1811页

作者： qiu, Xiaoying Jiang, Ting Wang, Ning Beijing Univ Posts & Telecommun Minist Educ Key Lab Universal Wireless Commun Beijing Peoples R China

In this study, the authors consider secure communications in multiuser wireless networks where full-duplex (FD) jamming operates to enhance physical layer security. The considered multiuser system is equipped with FD legitimate receivers in contrast to conventional frameworks where a half-duplex (HD) receiver is at hand. This study investigates an alternative solution in which the authors take advantage of FD capability of the receivers to send jamming signals against the eavesdropper. Under these assumptions, the impact of self-interference and channel interference on physical layer security is investigated. They derive the expressions of secrecy outage probability and ergodic secrecy rate in the proposed system. Two FD jammer selection schemes are proposed to further improve the security. In addition, they apply a reinforcement learning technology, called q-learning, to model the interaction between the source and multiple jamming users. The preliminary results show that the application of FD jamming and user selection scheme leads to a significant improvement in the wireless network security.

关键词： jamming telecommunication security telecommunication network reliability wireless channels learning (artificial intelligence) cooperative communication radio networks probability MIMO communication jamming signals physical layer security secrecy outage probability ergodic secrecy rate FD jammer selection schemes reinforcement learning technology source multiple jamming users user selection scheme wireless network security safeguarding multiuser communication full-duplex jamming q-learning algorithm secure communications wireless networks considered multiuser system FD legitimate receivers conventional frameworks half-duplex receiver alternative solution FD capability

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