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2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Chauhan, Tania Renjith, P.N. Vellore Institute of Technology Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348460

As 6G networks take off, there is an increasing need for effective system optimization strategies that can decrease end-to-end (e2e) delay and improve the general efficiency of the network. The sarsa (State-Action-Reward-State-Action) algorithm, which can learn from the network ecosystem and optimize network parameters accordingly, is the foundation of the unique real-time network optimization approach that is suggested in this research. With the aid of a simulation environment that replicates the actual 6G network environment, we assess the suggested approach. Our findings show that, the sarsa algorithm may substantially decrease e2e delay and improve network performance. In general, this research offers an intriguing plan for network optimization in 6G networks and emphasizes the sarsa algorithm's potential for improving real-time network reliability. © 2024 IEEE.

关键词： 6G e2e delay Network Optimization sarsa algorithm

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Research for UAV Path Planning Method Based on Guided sarsa algorithm 2

Research for UAV Path Planning Method Based on Guided Sarsa ...

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2nd IEEE International Conference on Software Engineering and Artificial Intelligence (SEAI) / 7th International Workshop on Pattern Recognition (IWPR)

作者： He Boming Lin Wei Mei Fuzeng Fan Huahao Wuhan Univ Technol Wuhan Peoples R China

ISBN: (数字)9781665482233

ISBN: (纸本)9781665482233

As unmanned aerial vehicles (UAV) are widely used in unknown and complex environments, their path planning capabilities face higher requirements. In many cases, UAV cannot obtain the environmental information of the target area in advance, and the reinforcement learning (RL) algorithm to solve this problem faces the problem of slow convergence speed. To solve this problem, a UAV path planning method based on guided sarsa algorithm is proposed, which defines the return function based on position information and improves the status update strategy. Simulation results show that the proposed method can realize fast path planning of UAV in static environment. Compared with Q learning and unimproved sarsa algorithm, the obtained path length is shortened by 18 steps and 2 steps. At the same time, after 2000 iterations, only the results obtained by this method have obvious convergence. The convergence speed of guided sarsa algorithm is relatively accelerated. At the same time, the trajectory tracking process of UAV is realized by MATLAB platform, which proves the practicability and feasibility of the algorithm.

关键词： UAV Path planning goal orientation reinforcement learning sarsa algorithm

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Time-Sensitive Network Simulation for In-Vehicle Ethernet Using sarsa algorithm

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WORLD ELECTRIC VEHICLE JOURNAL 2024年第1期15卷 21页

作者： Huang, Chen Wang, Yiqi Zhang, Yuxin Jilin Univ Coll Automot Engn Changchun 130012 Peoples R China Jiangsu Univ Inst Automot Engn Zhenjiang 212013 Peoples R China

In order to more accurately analyze the problem of time delay simulation and calculation in the time-sensitive network (TSN) of vehicular Ethernet, a TSN reservation class data delay analysis model improved based on the State-Action-Reward-State-Action (sarsa) reinforcement learning algorithm is proposed. Firstly, the TSN data queue forwarding delay model and reservation class data delay analysis intelligent body model are established, then the TSN traffic scheduling mechanism is improved by the sarsa reinforcement learning algorithm, and the improved TSN network reservation class data analysis model is established for the uncertainty of traffic scheduling in the network;finally, the fitting performance of the proposed method is verified by simulation and experimental validation. The results show that the deviation between the two is less than 5% under different BE loads, i.e., the established reservation class data delay analysis model is able to correctly fit the scheduling mechanism of the vehicle-mounted TSN network, which proves the reasonableness of the model simulation.

关键词： in-vehicle Ethernet time-sensitive networks sarsa algorithm simulation computation intelligent body modeling

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Residual sarsa algorithm with function approximation

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第1-Sup期22卷 795-807页

作者： Fu Qiming Hu Wen Liu Quan Luo Heng Hu Lingyao Chen Jianping Suzhou Univ Sci & Technol Inst Elect & Informat Engn Suzhou 215009 Peoples R China Suzhou Univ Sci & Technol Jiangsu Key Lab Intelligent Bldg Energy Efficienc Suzhou 215009 Jiangsu Peoples R China Suzhou Univ Sci & Technol Suzhou Key Lab Mobile Networking & Appl Technol Suzhou 215009 Jiangsu Peoples R China Soochow Univ Sch Comp Sci & Technol Suzhou 215000 Jiangsu Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Jilin Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing 210000 Jiangsu Peoples R China

In this work, we proposed an efficient algorithm named the residual sarsa algorithm with function approximation (FARS) to improve the performance of the traditional sarsa algorithm, and we use the gradient-descent method to update the function parameter vector. In the learning process, the Bellman residual method is adopted to guarantee the convergence of the algorithm, and a new rule for updating vectors of action-value functions is adopted to solve unstable and slow convergence problems. To accelerate the convergence rate of the algorithm, we introduce a new factor, named the forgotten factor, which can help improve the robustness of the algorithm's performance. Based on two classical reinforcement learning benchmark problems, the experimental results show that the FARS algorithm has better performance than other related reinforcement learning algorithms.

关键词： Reinforcement learning sarsa algorithm Function approximation Gradient descent Bellman residual

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An Online Reinforcement Learning-Based Energy Management Strategy for Microgrids With Centralized Control

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第1期61卷 1501-1510页

作者： Meng, Qinglin Hussain, Sheharyar Luo, Fengzhang Wang, Zhongguan Jin, Xiaolong State Grid Tianjin Elect Power Co Comprehens Serv Ctr Tianjin 300010 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Tiangong Univ Sch Elect Engn Tianjin 300387 Peoples R China Minist Educ Key Lab Smart Grid Tianjin 300072 Peoples R China Key Lab Smart Energy & Informat Technol Tianjin Mu Tianjin 300072 Peoples R China Zhejiang Univ Inst Marine Elect & Intelligent Syst Ocean Coll Zhoushan 316000 Peoples R China

To address the issue of significant unpredictability and intermittent nature of renewable energy sources, particularly wind and solar power, this paper introduces a novel optimization model based on online reinforcement learning. Initially, an energy management optimization model is designed to achieve plan adherence and minimize energy storage (ES) operation costs, taking into account the inherent challenges of wind power-photovoltaic energy storage systems (WPESS). An online reinforcement learning framework is employed, which defines various state variables, action variables, and reward functions for the energy management optimization model. The state-action-reward-state-action (sarsa) algorithm is applied to learn the joint scheduling strategy for the microgrid system, utilizing its iterative exploration mechanisms and interaction with the environment. This strategy aims to accomplish the goals of effective power tracking and reduction of storage charging and discharging. The proposed method's effectiveness is validated using a residential community with electric vehicle (EV) charging loads as a test case. Numerical analyses demonstrate that the approach is not reliant on traditional mathematical models and adapts well to the uncertainties and complex constraints of the WPESS, maintaining a low-cost requirement while achieving computational efficiency significantly higher than that of the model predictive control (MPC) and deep Q-network (DQN) algorithm.

关键词： Reinforcement learning Renewable energy sources Microgrids Costs Monitoring Wind power generation Optimization models Grid integration optimal energy management reinforcement learning sarsa algorithm wind-solar-storage microgrid

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User preference-based intelligent road route recommendation using sarsa and dynamic programming

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Journal of Control and Decision 2023年第3期10卷 443-453页

作者： Roopa Ravish Shanta Rangaswamy Arpitha V Vasuprada U PES University BengaluruIndia Rashtreeya Vidyalaya College of Engineering BengaluruIndia

Traffic congestion is one of the main challenges in transportation engineering. It directly impactsthe economy by increasing travel time and affecting the environment by excessive fuel consumptionand emission. Road route recommendation to overcome the congestion by alternativeroute suggestions has gained high importance. The existing route recommendation systems areproposed using the reinforcement learning algorithm (Q-learning). The techniques suggestedin this paper are state-action-reward-state-action (sarsa) algorithm and dynamic programming(DP) to guide the commuters to reach the destination with an optimal solution. The algorithmconsiders travel time, cost, flexibility, and traffic intensity as the user preference attributes torecommend an optimal route. The recommended system is implemented by building a roadnetwork graph. We assign values to each user preference attribute along the edges, which cantake high(1) or low(0) values. By considering these values, the system recommends the *** proposed system performance is evaluated based on computation time, cumulative reward,and accuracy. The results show that DP outperforms the sarsa algorithm.

关键词： Intelligent transport system machine learning techniques in ITS sarsa algorithm dynamic programming route guidance system travel time prediction traveller information system

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Multi-task Scheduling of Multiple Agricultural Machinery via Reinforcement Learning and Genetic algorithm 20th

Multi-task Scheduling of Multiple Agricultural Machinery via...

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20th International Conference on Intelligent Computing (ICIC)

作者： Li, Lihang Jia, Liruizhi Liu, Shengquan Kong, Bo Liu, Yuan Xinjiang Univ Sch Comp Sci & Technol Urumqi 830046 Peoples R China

ISBN: (纸本)9789819755776;9789819755783

Scheduling of agricultural machinery is a crucial aspect of smart agriculture. This paper addresses the multi-task scheduling problem for multiple agricultural machinery, which is challenging and belongs to NP-hard. However, traditional intelligent algorithms solve this problem by fixing their key parameters in a preset way. This may cause efficiency and quality of the solution to be unable to meet the production requirements. Therefore, this paper proposes a self-learning algorithm (SLA), which combines the sarsa algorithm and the genetic algorithm. Firstly, a combined model is constructed. Secondly, the Markov decision process's state, action, reward, and strategy are designed. Finally, the performance of SLA in solving multi-tasks of multiple agricultural machinery is compared to baselines using eighteen groups of instances with different scales. The experimental results show that the proposed SLA significantly outperforms baselines in solving multi-task scheduling of multiple agricultural machinery. This finding can provide a reasonable scheduling scheme for the production of large-scale unmanned farms.

关键词： scheduling problem of agricultural machinery Self-Learning algorithm (SLA) Genetic algorithm (GA) sarsa algorithm

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Peer-to-peer Electricity Transaction Decisions of the User-side Smart Energy System Based on the sarsa Reinforcement Learning

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CSEE Journal of Power and Energy Systems 2022年第3期8卷 826-837页

作者： Dan Wang Bo Liu Hongjie Jia Ziyang Zhang Jingcheng Chen Deyu Huang Key Laboratory of Smart Grid of Ministry of Education Tianjin UniversityTianjin 300072China Key Laboratory of Smart Energy&Information Technology of Tianjin Municipality Tianjin 30072China State Grid Jiangsu Power Co.Ltd. Zhenjiang Power Supply CompanyZhenjiang 212000Jiangsu ProvinceChina State Grid Tianjin Electric Power Company Hebei DistrictTianjin 300010China

With the deep integration of advanced information technologies,such as artificial intelligence and traditional energy technologies,smart energy systems have been proposed as a method to provide the best solution for the coordination,balance,and control of the entire energy *** a new way of energy balance and interaction in the user side energy market,a peer-to-peer(P2P)electricity transaction can effectively promote energy sharing within the user group and improve the economic benefits of users participating in the energy *** learning(RL)is an artificial intelligence method in which agents continuously acquire relevant experience and knowledge during the interaction with the environment,automatically update their decision-making behavior;and achieve maximum *** is a suitable approach for P2P transaction decision analysis of small-scale users in the context of smart ***,this paper establishes a P2P transaction model that includes a participant model,equipment model and price ***,the transaction problem is equivalent to a Markov decision process(MDP)and each learning element model is ***,the MDP problem is solved and analyzed using the sarsa RL algorithm with average discrete ***,a case study of a community with multiple users is conducted to verify the effectiveness,economy,and security of the RL method in solving energy storage action selection and transaction decision problems of energy storage users.

关键词： Peer-to-peer electricity transaction reinforcement learning smart energy system sarsa algorithm

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Application of sarsa-Based Reinforcement Learning Approach for Resource Allocation in Vehicular Edge Computing 10

Application of SARSA-Based Reinforcement Learning Approach f...

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10th International Conference on Optimization and Applications, ICOA 2024

作者： Myyara, Marouane Darif, Anouar Farchane, Abderrazak Sultan Moulay Slimane University Limati Laboratory Beni Mellal Morocco

ISBN: (纸本)9798350387353

Vehicular Edge Computing (VEC) holds significant promise for enhancing the performance and resource utilization of Internet of Vehicles (loV) applications. However, the dynamic nature of vehicular networks, characterized by high mobility and limited capabilities of VEC servers, poses significant challenges for resource allocation. This paper proposes a Reinforcement Learning (RL) based approach, employing the sarsa algorithm, to learn optimal resource allocation strategies in Multi-access Edge Computing (MEC) environments designed for vehicular networks. Our strategy aims to minimize service delays and balance resource utilization effectively by dynamically deciding whether to offload tasks to edge servers or cloud servers. We evaluate the proposed approach using the EdgeCloudSim simulator, comparing its performance against conventional static and heuristic resource allocation methods. Simulation results, considering various parameters and application characteristics, demonstrate the superior efficiency of our sarsa-based resource allocation solution for VEC compared to traditional methods, in terms of overall service time and resource utilization. © 2024 IEEE.

关键词： Multi-access Edge Computing Reinforcement Learning Resource Allocation sarsa algorithm Vehicular Edge Computing

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sarsa-Based Output Tracking Problem of Stochastic Boolean Control Networks 41

Sarsa-Based Output Tracking Problem of Stochastic Boolean Co...

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第41届中国控制会议

作者： Heng Zhu Ying Wang Hongwei Chen School of Information Science and Technology Donghua University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,the output tracking problem(OTP) of stochastic Boolean control networks(SBCNs) is investigated by taking advantage of a reinforcement learning(RL) ***,an algebraic expression of SBCNs is constructed based on the semi-tensor product(STP) of *** working out states that can reach a given constant reference signal in one time step,the OTP is converted into a set stabilization problem(SSP).Secondly,a novel algorithm for exploring maximal control invariant subset(MCIS) is *** investigate the optimal control,the SSP is transformed into a path optimization problem(POP).Thirdly,a recursive algorithm of model-free RL,namely sarsa,is adopted for resolving the *** to an optimal control sequence calculated by the sarsa algorithm,an optimal state feedback controller is designed for resolving the ***,the application scope of sarsa algorithm is expanded by introducing an undesirable ***,a SBCN model of a biological network is adopted as an instance to illustrate the feasibility of the algorithms adopted.

关键词： Stochastic Boolean control networks maximal control invariant subset set stabilization sarsa algorithm

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