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6th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Ning, Longfei Liu, Feiyu Wang, Zhengfeng Feng, Kai Wang, Beibei Southeast Univ Sch Elect Engn Nanjing Peoples R China State Grid Corp China State Grid Anhui Elect Power Co Ltd Hefei Peoples R China State Grid Corp China China Elect Power Res Inst Nanjing Peoples R China

ISBN: (纸本)9798350373486;9798350373479

Uncertainty in the output of renewable energy sources can lead to fluctuations in the bidding revenue of their participation in the electricity market. To address this problem, a stochastic optimization model for strategic bidding of renewable energy unit considering output uncertainty is established. The conditional value-at-risk and soft actor-critic algorithm are combined to form the CVaR-SAC strategic bidding algorithm that can control the risk of fluctuation in bidding revenue, and the effectiveness of the algorithm is verified through cases.

关键词： conditional value-at-risk electricity market renewable energy bidding strategy soft actor-critic algorithm

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O³GPT: A Guidance-Oriented Periodic Testing Framework with Online Learning, Online Testing, and Online Feedback 29th

O<SUP>3</SUP>GPT: A Guidance-Oriented Periodic Testing Frame...

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29th International Conference on Neural Information Processing

作者： Ren, Yimeng Shang, Yuhu Liang, Kun Zhang, Xiankun Zhang, Yiying Tianjin Univ Sci & Technol Coll Artificial Intelligence Tianjin 300457 Peoples R China

ISBN: (纸本)9789819916382;9789819916399

Periodic testing (PT) is part and parcel of instructional process, which targets at measuring student proficiency level on specific stage. In general, most previous PTs follow an inflexible offline-policy method, which can hardly adjust testing procedure using the online feedback instantly. In this paper, we develop a dynamic and executed online periodic testing framework called O-3 GPT, which selects the most suitable questions step by step, depending on student's previous timestep's real-time feedback. To begin with, we employ a stacked GRU to update student's state representation instantly, which could well capture the long-term dynamic nature from their past learning trajectories, leading to the testing agent perform effective periodic testing. Subsequently, in Stage2, O(3)GPT incorporates a flexible testing-specific reward function into the soft actor-critic algorithm (SAC) to guarantee the rationality of all selected questions. Finally, to set up the online feedback, we test O(3)GPT on an on-line simulated environment which can model qualitative development of knowledge proficiency. The results of our experiment conducted on two well-established student response datasets indicate that O(3)GPT outperforms state-of-the-art baselines in PT task.

关键词： Periodic testing Online learning soft actor-critic algorithm

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Research on Inverted Pendulum Control Strategy Based on Deep Reinforcement Learning 3

Research on Inverted Pendulum Control Strategy Based on Deep...

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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Zheng, Jiehong Wang, Xinzui Yang, Yanli Jihua Medical Institute Jihua Laboratory Foshan528251 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Suzhou215163 China

ISBN: (纸本)9798350382426

Aiming at the control planning of inverted pendulum task, the planning strategy based on soft actor-critic (SAC) algorithm was studied. An agent based on actor-critic framework is designed, which takes inverted pendulum state as input and planned target position as output as controller. The agent has a total of 5 neural networks, namely, action network actor, 2 current evaluation networks and 2 target evaluation networks. actor network outputs the next planned position according to the current inverted pendulum state, and the current critic network and target critic network respectively output the value evaluation of the current action and the next action according to the reward feedback from the environment. The PD control method is used to convert the planned target position of the mobile network output into the control voltage value, and actually control the inverted swing. Simulation results show that the proposed method is effective and efficient. © 2023 IEEE.

关键词： deep reinforcement learning motion control neural network rotary inverted pendulum soft actor-critic algorithm

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Reinforcement learning for Hybrid Disassembly Line Balancing Problems

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NEUROCOMPUTING 2024年 569卷

作者： Wang, Jiacun Xi, Guipeng Guo, Xiwang Liu, Shixin Qin, Shujin Han, Henry Monmouth Univ West Long Branch NJ 07764 USA Liaoning Petrochem Univ Fushun 113005 Peoples R China Northeastern Univ Shenyang 110167 Peoples R China Shangqiu Normal Univ Shangqiu 476000 Peoples R China Baylor Univ Waco TX 76798 USA

With the rapid development of the economy and technology, the rate of product replacement has accelerated, resulting in a large number of products being discarded. Disassembly is an important way to recycle waste products, which is also helpful to reduce manufacturing costs and environmental pollution. The combination of a single-row linear disassembly line and a U-shaped disassembly line presents distinctive advantages within various application scenarios. The Hybrid Disassembly Line Balancing Problem (HDLBP) that considers the requirement of multi-skilled workers is addressed in this paper. A mathematical model is established to maximize the recovery profit according to the characteristics of the proposed problem. To facilitate the search for optimal solution, a new strategy for agents in reinforcement learning to interact with complex and changeable environments in real-time is developed, and deep reinforcement learning is used to complete the distribution of multi-products and disassembly tasks. On this basis, we propose a soft actor-critic (SAC) algorithm to effectively address this problem. Compared with the Deep Deterministic Policy Gradient (DDPG) algorithm, Advantage actor-critic (A2C) algorithm, and Proximal Policy Optimization (PPO) algorithm, the results show that the SAC can get the approximate optimal result on small-scale cases. The performance of SAC is also better than DDPG, PPO, and A2C in solving large-scale disassembly cases.

关键词： Hybrid disassembly line balancing problem soft actor-critic algorithm Deep reinforcement learning Optimization

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A deep reinforcement learning based distributed multi-UAV dynamic area coverage algorithm for complex environment

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NEUROCOMPUTING 2024年 595卷

作者： Xiao, Jian Yuan, Guohui Xue, Yuxi He, Jinhui Wang, Yaoting Zou, Yuanjiang Wang, Zhuoran Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Yangtze Delta Reg Inst Quzhou Quzhou 324000 Zhejiang Peoples R China

Dynamic area coverage algorithm is often used in multiple unmanned aerial vehicle (multi-UAV) systems to realize searching or monitoring an area of interest (AOI). Improving coverage efficiency is one of the important research issues of dynamic area coverage. In this paper, we propose a distributed dynamic area coverage algorithm based on a reinforcement learning (RL) algorithm to enhance the coverage efficiency in complex application environments. A coverage information fusion based discrete soft actor-critic algorithm (herein FDSAC) is proposed to solve the non -stationary problem of RL algorithm in area coverage task under the communication constraint environment. Considering the equivalence of UAVs in the area coverage tasks, we introduce a multi-UAV cooperative learning method for FDSAC by sharing the strategy and experience of the UAVs to accelerate the learning speed of FDSAC in multi-UAV system. With the FDSAC algorithm, the UAVs can intelligently select the best coverage points and achieve almost optimal coverage point planning for the entire coverage process. In addition, we also provide a coverage point adjustment method based on the distribution of obstacles to enable the proposed algorithm adapt to obstacle environments. The experimental results demonstrate that the proposed algorithm can efficiently complete the area coverage task in complex communication and stochastic control noise environment. The good scalability, environment adaptability, and practicality of our algorithm have been also confirmed in different complex environments of different experimental platforms.

关键词： Dynamic area coverage Multiple unmanned aerial vehicle system Reinforcement learning soft actor-critic algorithm Complex environment

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Learning-Based Robust Anomaly Detection in the Presence of Adversarial Attacks

Learning-Based Robust Anomaly Detection in the Presence of A...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Zhong, Chen Gursoy, M. Cenk Velipasalar, Senem Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

ISBN: (纸本)9781665442664

To address the anomaly detection problem in the presence of noisy sensor observations and probing costs, we in this paper propose a soft actor-critic deep reinforcement learning framework. Moreover, considering adversarial jamming attacks, we design a generative adversarial network (GAN) based framework to identify the jammed sensors. To evaluate the proposed framework, we measure the performance in terms of detection accuracy, stopping time, and the total number of samples needed for detection. Via simulation results, we demonstrate the performances when soft actor-critic algorithms are sensitive to the probing cost and actively adapt to different environment settings. We analyze the impact of jamming attacks and identify the improvements achieved by GAN-based approach. We further provide comparisons between the performances of the proposed soft actor-critic and conventional actor-critic algorithms.

关键词： Anomaly detection controlled sensing reinforcement learning soft actor-critic algorithm GAN jamming attack

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Dynamic Path Planning for Mobile Robots with Deep Reinforcement Learning

Dynamic Path Planning for Mobile Robots with Deep Reinforcem...

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IFAC Workshop on Control for Smart Cities (CSC)

作者： Yang, Laiyi Bi, Jing Yuan, Haitao Beijing Univ Technol Sch Software Engn Fac Informat Technol Beijing 100124 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

Traditional path planning algorithms for mobile robots are not effective to solve high-dimensional problems, and suffer from slow convergence and complex modelling. Therefore, it is highly essential to design a more efficient algorithm to realize intelligent path planning of mobile robots. This work proposes an improved path planning algorithm, which is based on the algorithm of soft actor-critic (SAC). It attempts to solve a problem of poor robot performance in complicated environments with static and dynamic obstacles. This work designs an improved reward function to enable mobile robots to quickly avoid obstacles and reach targets by using state dynamic normalization and priority replay buffer techniques. To evaluate its performance, a Pygame-based simulation environment is constructed. The proposed method is compared with a Proximal Policy Optimization (PPO) algorithm in the simulation environment. Experimental results demonstrate that the cumulative reward of the proposed method is much higher than that of PPO, and it is also more robust than PPO. Copyright (C) 2022 The Authors.

关键词： Deep reinforcement learning path planning soft actor-critic algorithm continuous reward functions mobile robots

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Deep reinforcement learning-based controller for dynamic positioning of an unmanned surface vehicle

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COMPUTERS & ELECTRICAL ENGINEERING 2023年 110卷

作者： Yuan, Wei Rui, Xingwen Jiangsu Univ Sci & Technol Coll Automat Zhenjiang 212100 Peoples R China

Dynamic positioning (DP) system is of great significance for the unmanned surface vehicle (USV) to achieve fully autonomous navigation. Traditional control schemes have problems such as model accuracy, parameter tuning, and complex design. In addition, although the deep rein-forcement learning (DRL) is widely used in the field of vessel motion control, the learning effi-ciency is not high, and insufficient robustness in the face of changing environmental. In order to improve the anti-disturbance ability, robustness and convergence speed of the controller during training, a deep reinforcement learning control method based on priority experience replay (PER) is proposed for dynamic positioning of the USV. The mathematical models are established based on the kinematic and dynamic of the USV. Markov decision process (MDP) models are constructed according to the DP tasks. The simulation results show that compared with other DRL algorithms, the proposed method has higher reward value, faster convergence speed, higher control precision and smoother control output.

关键词： Unmanned surface vehicles Dynamic positioning Deep reinforcement learning soft actor-critic algorithm Prioritized experience replay

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Load Frequency Control of Interconnected Microgrid Enhanced by Deep Reinforcement Learning SAC-PID

Load Frequency Control of Interconnected Microgrid Enhanced ...

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第43届中国控制会议

作者： Zheng Liu Lijun Zhu Bolin Li Xuhui Wang School of Artificial Intelligence and Automation Huazhong University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In recent years, the incorporation of sustainable energy resources such as wind power has had a significant impact on the stability of microgrids. In this context, our research introduces a proficient method for load frequency regulation utilizing deep reinforcement learning(DRL). Firstly, a two-area interconnected microgrid frequency control model is constructed, including wind power generation, reheat steam turbines, generators and loads. Secondly, the proposed method uses a soft actor critic(SAC)with a double Q-network to tailor the parameters of the proportional integral derivative(PID) controller, to train a DRL agent to obtain optimal controller parameters for this two-area interconnection, and we used the area control error(ACE) as the controller's input to reduce fluctuations in both grid frequency and interconnection line power flows. Furthermore, we designed a reward function via the square of the ACE to obtain the best-suited parameters for the controller. Finally, the trained controllers are tested under step load disturbance and random load disturbance, verifying that the designed SAC-PID controller provides improved frequency control performance for microgrids compared with the classical PID controller and the PID controller optimized through the particle swarm optimization(PSO) algorithm.

关键词： Microgrid Load Frequency Control Deep Reinforcement Learning PID Controller soft actor-critic algorithm Area Control Error

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Comparison of Deep Reinforcement Learning and PID Controllers for Automatic Cold Shutdown Operation

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ENERGIES 2022年第8期15卷 2834页

作者： Lee, Daeil Koo, Seoryong Jang, Inseok Kim, Jonghyun Chosun Univ Dept Nucl Engn Gwangju 61452 South Korea Korea Atom Energy Res Inst Daejeon 34057 South Korea

Many industries apply traditional controllers to automate manual control. In recent years, artificial intelligence controllers applied with deep-learning techniques have been suggested as advanced controllers that can achieve goals from many industrial domains, such as humans. Deep reinforcement learning (DRL) is a powerful method for these controllers to learn how to achieve their specific operational goals. As DRL controllers learn through sampling from a target system, they can overcome the limitations of traditional controllers, such as proportional-integral-derivative (PID) controllers. In nuclear power plants (NPPs), automatic systems can manage components during full-power operation. In contrast, startup and shutdown operations are less automated and are typically performed by operators. This study suggests DRL-based and PID-based controllers for cold shutdown operations, which are a part of startup operations. By comparing the suggested controllers, this study aims to verify that learning-based controllers can overcome the limitations of traditional controllers and achieve operational goals with minimal manipulation. First, to identify the required components, operational goals, and inputs/outputs of operations, this study analyzed the general operating procedures for cold shutdown operations. Then, PID- and DRL-based controllers are designed. The PID-based controller consists of PID controllers that are well-tuned using the Ziegler-Nichols rule. The DRL-based controller with long short-term memory (LSTM) is trained with a soft actor-critic algorithm that can reduce the training time by using distributed prioritized experience replay and distributed learning. The LSTM can process a plant time-series data to generate control signals. Subsequently, the suggested controllers were validated using an NPP simulator during the cold shutdown operation. Finally, this study discusses the operational performance by comparing PID- and DRL-based controllers.

关键词： nuclear power plant autonomous operation artificial intelligence deep reinforcement learning soft actor-critic algorithm

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