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Full-Text Articles in Engineering
Task Scheduling For Internet Of Vehicles Based On Deep Reinforcement Learning In Edge Computing, Xiang Ju, Shengchao Su, Chaojie Xu, Beibei He
Task Scheduling For Internet Of Vehicles Based On Deep Reinforcement Learning In Edge Computing, Xiang Ju, Shengchao Su, Chaojie Xu, Beibei He
Journal of System Simulation
Abstract: Aiming at the offloading and execution of delay-constrained computing tasks for internet of vehicles in edge computing, a task scheduling method based on deep reinforcement learning is proposed. In multi-edge server scenario, a software-defined network-aided internet of vehicles task offloading system is built. On this basis, the task scheduling model of vehicle computation offloading is given. According to the characteristics of task scheduling, a scheduling method based on an improved pointer network is designed. Considering the complexity of task scheduling and computing resource allocation, the deep reinforcement learning algorithm is used to train the pointer network. The vehicle offloading …
Intelligent Air Defense Task Assignment Based On Assignment Strategy Optimization Algorithm, Jiayi Liu, Gang Wang, Qiang Fu, Xiangke Guo, Siyuan Wang
Intelligent Air Defense Task Assignment Based On Assignment Strategy Optimization Algorithm, Jiayi Liu, Gang Wang, Qiang Fu, Xiangke Guo, Siyuan Wang
Journal of System Simulation
Abstract: Aiming at the insufficient solving speed of assignment strategy optimization algorithm in largescale scenarios, deep reinforcement learning is combined with Markov decision process to carry out the intelligent large-scale air defense task assignment. According to the characteristics of large-scale air defense operations, Markov decision process is used to model the agent and a digital battlefield simulation environment is built. Air defense task assignment agent is designed and trained in digital battlefield simulation environment through proximal policy optimization algorithm. The feasibility and advantage of the method are verified by taking a large-scale ground-to-air countermeasure mission as an example.
Obstacle Avoidance Path Planning And Simulation Of Mobile Picking Robot Based On Dppo, Junqiang Lin, Hongjun Wang, Xiangjun Zou, Po Zhang, Chengen Li, Yipeng Zhou, Shujie Yao
Obstacle Avoidance Path Planning And Simulation Of Mobile Picking Robot Based On Dppo, Junqiang Lin, Hongjun Wang, Xiangjun Zou, Po Zhang, Chengen Li, Yipeng Zhou, Shujie Yao
Journal of System Simulation
Abstract: Aiming at the autonomous decision-making difficulty of mobile picking robots in random and changeable complicated path environment during field operations, an autonomous obstacle avoidance path planning method based on deep reinforcement learning is propose. By setting the state space and action space and using the artificial potential field method to design the reward function, an obstacle penalty coefficient setting method based on collision cone collision avoidance detection is proposed to improve the autonomous collision avoidance ability. A virtual simulation system is constructed, in which the learning and training of the mobile picking robot is carried out and verified by …
Intelligent Path Planning For Mobile Robots Based On Sac Algorithm, Laiyi Yang, Jing Bi, Haitao Yuan
Intelligent Path Planning For Mobile Robots Based On Sac Algorithm, Laiyi Yang, Jing Bi, Haitao Yuan
Journal of System Simulation
Abstract: Aiming at the high dimension, slow convergence and complex modelling of traditional path planning algorithms for mobile robots, a new intelligent path planning algorithm is proposed, which is based on deep reinforcement learning soft actor-critic (SAC) algorithm to save the poor performance of robot in complicated environments with static and dynamic obstacles. An improved reward function is designed to enable mobile robots to quickly avoid obstacles and reach targets by using state dynamic normalization and priority experience pool techniques. To evaluate the performance, a pygame-based simulation environment is constructed. Compared with proximal policy optimization(PPO) algorithm, experimental …
Joint Optimization Strategy Of Computing Offloading And Edge Caching For Intelligent Connected Vehicles, Fei Ding, Yuchen Sha, Ying Hong, Xiao Kuai, Dengyin Zhang
Joint Optimization Strategy Of Computing Offloading And Edge Caching For Intelligent Connected Vehicles, Fei Ding, Yuchen Sha, Ying Hong, Xiao Kuai, Dengyin Zhang
Journal of System Simulation
To guarantee the low-delay communication of intelligent connected vehicles, the V2X channel model and the multi-access edge computing (MEC) technology, are used to carry out the research of the joint optimization strategy of computing offloading and edge caching.An intelligent connected vehicle with task offloading and edge caching model least-deep deterministic policy gradient(L-DDPG) is developed.By integrating the vehicular local and edge computing resources, the classification processing of different computing tasks in V2X scenarios is supported.The vehicular computing request is prejudged by edge platform to ensure the rapid response of continuous homogeneous computing tasks. Combining with the least recently …
Deep Reinforcement Learning-Based Control Strategy For Boost Converter, Yuxuan Dai, Chenggang Cui
Deep Reinforcement Learning-Based Control Strategy For Boost Converter, Yuxuan Dai, Chenggang Cui
Journal of System Simulation
Abstract: In view of the problems of model uncertainty and nonlinearity in bus voltage stability control of Boost converter, an intelligent control strategy based on model-free deep reinforcement learning(RL) is proposed. RL double DQN(DDQN) algorithm and deep deterministic policy gradient(DDPG) algorithm are used, and the Boost converter controller is designed. The state, action space, reward function, and neural network are also designed to improve the dynamic performance of the controller. The joint simulation of the Boost converter model and RL agent is realized by RL modelica(RLM …
Research Progress Of Opponent Modeling Based On Deep Reinforcement Learning, Haotian Xu, Long Qin, Junjie Zeng, Yue Hu, Qi Zhang
Research Progress Of Opponent Modeling Based On Deep Reinforcement Learning, Haotian Xu, Long Qin, Junjie Zeng, Yue Hu, Qi Zhang
Journal of System Simulation
Abstract: Deep reinforcement learning is an agent modeling method with both deep learning feature extraction ability and reinforcement learning sequence decision-making ability, which can make up for the depleted non-stationary adaptation, complex feature selection and insufficient state-space representation ability of traditional opponent modeling. The deep reinforcement learning-based opponent modeling methods are divided into two categories, explicit modeling and implicit modeling, and the corresponding theories, models, algorithms and applicable scenarios are sorted out according to the categories. The applications of deep reinforcement learning-based opponent modeling techniques on different fields are introduced. The key problems and future development are summarized to provide …