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Trajectory Optimization and Power Allocation Scheme Based on DRL in Energy Efficient UAV-Aided Communication networks

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Chinese Journal of Electronics 2022年第3期31卷 397-407页

作者： WANG Chaowei CUI Yuling DENG Danhao WANG Weidong JIANG Fan School of Electronic Engineering Beijing University of Posts and Telecommunications Key Laboratory of Universal Wireless Communications Ministry of Education School of Telecommunications and Information Engineering Xi'an University of Posts and Telecommunications

With flexibility, convenience and mobility, unmanned aerial vehicles(UAVS) can provide wireless communication networks with lower costs, easier deployment, higher network scalability and larger *** paper proposes the deep deterministic policy gradient algorithm to jointly optimize the power allocation and flight trajectory of UAV with constrained effective energy to maximize the downlink throughput to ground users. To validate the proposed algorithm, we compare with the random algorithm, q-learning algorithm and deep q network algorithm. The simulation results show that the proposed algorithm can effectively improve the communication quality and significantly extend the service time of UAV. In addition, the downlink throughput increases with the number of ground users.

关键词： unmanned aerial vehicles remotely operated vehicles trajectory optimization q-learning algorithm power allocation scheme flight trajectory communication quality optimisation mobile robots random algorithm deep q network algorithm learning systems constrained effective energy ground users autonomous aerial vehicles network scalability resource allocation UAVS energy efficient UAV-aided communication networks gradient methods deep deterministic policy gradient algorithm trajectory control radio networks wireless communication networks

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deep q networks-based optimization of emergency resource scheduling for urban public health events

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NEURAL COMPUTING & APPLICATIONS 2023年第12期35卷 8823-8832页

作者： Zhao, Xianli Wang, Guixin Hubei Normal Univ Res Ctr Transformat & Dev Resource Exhausted Citi Huangshi 435002 Hubei Peoples R China Wenzhou Business Coll Univ Sch Informat Engn Wenzhou 325035 Zhejiang Peoples R China

In today's severe situation of the global new crown virus raging, there are still efficiency problems in emergency resource scheduling, and there are still deficiencies in rescue standards. For the happiness and well-being of people's lives, adhering to the principle of a community with a shared future for mankind, the emergency resource scheduling system for urban public health emergencies needs to be improved and perfected. This paper mainly studies the optimization model of urban emergency resource scheduling, which uses the deep reinforcement learning algorithm to build the emergency resource distribution system framework, and uses the deep q network path planning algorithm to optimize the system, to achieve the purpose of optimizing and upgrading the efficient scheduling of emergency resources in the city. Finally, through simulation experiments, it is concluded that the deep learning algorithm studied is helpful to the emergency resource scheduling optimization system. However, with the gradual development of deep learning, some of its disadvantages are becoming increasingly obvious. An obvious flaw is that building a deep learning-based model generally requires a lot of CPU computing resources, making the cost too high.

关键词： deep learning Emergency resource scheduling deep reinforcement learning algorithm Path planning deep q network algorithm

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Research on intelligent fault diagnosis for railway point machines using deep reinforcement learning

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Transportation Safety and Environment 2024年第4期6卷 75-86页

作者： Shuai Xiao qingsheng Feng Xue Li Hong Li School of Automation and Electrical Engineering Dalian Jiaotong UniversityDalian 116028LiaoningChina School of Rail Transportation Shandong Jiaotong UniversityJinan 250357ShandongChina School of Software Dalian Jiaotong UniversityDalian 116028LiaoningChina

The advanced diagnosis of faults in railway point machines is crucial for ensuring the smooth operation of the turnout conversion system and the safe functioning of *** processing and deep learning-based methods have been extensively explored in the realm of fault *** these approaches effectively extract fault features and facilitate the creation of end-to-end diagnostic models,they often demand considerable expert experience and manual intervention in feature selection,structural construction and parameter optimization of neural *** reliance on manual efforts can result in weak generalization performance and a lack of intelligence in the *** address these challenges,this study introduces an intelligent fault diagnosis method based on deep reinforcement learning(DRL).Initially,a one-dimensional convolutional neural network agent is established,leveraging the specific characteristics of point machine fault data to automatically extract diverse features across multiple ***,deep q network is incorporated as the central component of the diagnostic *** fault classification interactive environment is meticulously designed,and the agent training network is *** extensive interaction between the agent and the environment using fault data,satisfactory cumulative rewards and effective fault classification strategies are *** results demonstrate the proposed method’s high efficacy,with a training accuracy of 98.9%and a commendable test accuracy of 98.41%.Notably,the utilization of DRL in addressing the fault diagnosis challenge for railway point machines enhances the intelligence of diagnostic process,particularly through its excellent independent exploration capability.

关键词： fault diagnosis railway point machines one-dimensional convolutional neural network deep q network algorithm

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AGV Path Planning Model based on Reinforcement Learning

AGV Path Planning Model based on Reinforcement Learning

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Chinese Automation Congress (CAC)

作者： Liao, Xiaofei Wang, Yang Xuan, Yiliang Wu, Dequan Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Donghua Univ Engn Res Ctr Digitized Text & Fash Technol Minist Educ Shanghai 201620 Peoples R China

ISBN: (纸本)9781728176871

With the rapid growth of logistics transportation, automated guided vehicle (AGV) technologY has developed speedily. Path planning is one of the key research topics of AGV. It is difficult to plan an optimal path from starting position to target position for AGV in the complex environment. In this paper, reinforcement learning technology is introduced to solve the problem that it is difficult to model AGV path planning due to complex and unknown environment. The Sarsa algorithm based on simulated annealing strategy can effectively guide ACV to plan the optimal path in the grid graph, and improve the success rate. Aiming at the problem that the traditional reinforcement learning algorithm processes data insufficiently in case of large-scale state space, the potential field method combined with deep q-network algorithm is proposed for AGV path planning. The algorithm can effective!) guide AGV to carry out optimal path planning, and solve the problem that the traditional reinforcement learning algorithm can not deal with complex space. Finally, these algorithms are applied to the ACV path planning system to simulate the motion state of a single AGV from the loading point to the unloading point. It verifies that our algorithms can effectively implement the AGV intelligent path planning and improve the efficiency of warehousing logistics.

关键词： AGV path planning reinforcement learning Sarsa algorithm deep q network algorithm

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Minimization of Energy and Service Latency Computation Offloading using Neural network in 5G NOMA System

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INTERNATIONAL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS 2023年第4期69卷 661-667页

作者： Suprith, P. G. Ahmed, Mohammed Riyaz REVA Univ Bangalore Karnataka India REVA Univ Bangalore Karnataka India HKBK Coll Engn Bangalore Karnataka India

The future Internet of Things (IoT) era is anticipated to support computation-intensive and time-critical applications using edge computing for mobile (MEC), which is regarded as promising technique. However, the transmitting uplink performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission power of IoT devices. Using edge computing for mobile (MEC) to offload tasks becomes a crucial technology to reduce service latency for computation-intensive applications and reduce the computational workloads of mobile devices. Under the restrictions of computation latency and cloud computing capacity, our goal is to reduce the overall energy consumption of all users, including transmission energy and local computation energy. In this article, the deep q network algorithm (DqNA) to deal with the data rates with respect to the user base in different time slots of 5G NOMA network. The DqNA is optimized by considering more number of cell structures like 2, 4, 6 and 8. Therefore, the DqNA provides the optimal distribution of power among all 3 users in the 5G network, which gives the increased data rates. The existing various power distribution algorithms like frequent pattern (FP), weighted least squares mean error weighted least squares mean error (WLSME), and Random Power and Maximal Power allocation are used to justify the proposed DqNA technique. The proposed technique which gives 81.6% more the data rates when increased the cell structure to 8. Thus 25% more in comparison to other algorithms like FP, WLSME Random Power and Maximal Power allocation.

关键词： Mobile edge computing deep q network algorithm Latency Optimized Computation Offloading 5G

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Implementing Game Strategies Based on Reinforcement Learning 20

Implementing Game Strategies Based on Reinforcement Learning

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Proceedings of the 6th International Conference on Robotics and Artificial Intelligence

作者： Botong Liu Palmer Trinity School United States

ISBN: (纸本)9781450388597

Artificial intelligence (AI) technology such as reinforcement learning is increasingly used in playing game in recent years. A deep reinforcement learning model was used to play the game Flappy Bird. This paper aimed to let the computer play a simple game and get the corresponding data for AI learning. Game image was sequentially scaled, grayed, and adjusted for brightness. Before the current frame entered a state, the multi-dimensional image data of several frames of image superposition and combination was processed. deep q network algorithm realized the best action prediction of the game execution in a specific game state, and successfully converted a game decision problem into the classification and recognition problem of instant multi-dimensional images and solved it with a convolutional neural network. After analysis, computer players controlled by deep neural networks had better results than human players. This experiment was a model combined between a deep neural network model and reinforcement learning, and could be applied in other games.

关键词： deep q network algorithm Reinforcement learning Multi-dimensional image Flappy bird

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