Lyuchao Liao^1,2, Hankun Xiao^2,*, Pengqi Xing², Zhenhua Gan^1,2, Youpeng He², Jiajun Wang²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 557-576, 2024, DOI:10.32604/cmes.2024.047452

Abstract Autonomous driving has witnessed rapid advancement; however, ensuring safe and efficient driving in intricate scenarios remains a critical challenge. In particular, traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles, susceptibility to traffic flow bottlenecks, and imperfect data in perceiving environmental information, rendering them a vital issue in the practical application of autonomous driving. To address the traffic challenges, this work focused on complex roundabouts with multi-lane and proposed a Perception Enhanced Deep Deterministic Policy Gradient (PE-DDPG) for Autonomous Driving in the Roundabouts. Specifically, the model incorporates an enhanced variational… More >

Jian Dong, Haixin Wang, Junyou Yang^*, Liu Gao, Kang Wang, Xiran Zhou

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.1, pp. 319-340, 2022, DOI:10.32604/cmes.2022.020394

Abstract Integrated energy system optimization scheduling can improve energy efficiency and low carbon economy. This paper studies an electric-gas-heat integrated energy system, including the carbon capture system, energy coupling equipment, and renewable energy. An energy scheduling strategy based on deep reinforcement learning is proposed to minimize operation cost, carbon emission and enhance the power supply reliability. Firstly, the low-carbon mathematical model of combined thermal and power unit, carbon capture system and power to gas unit (CCP) is established. Subsequently, we establish a low carbon multi-objective optimization model considering system operation cost, carbon emissions cost, integrated demand response, wind and photovoltaic curtailment,… More >

Sun Yang-Yang, Yao Jun-Ping^*, Li Xiao-Jun, Fan Shou-Xiang, Wang Zi-Wei

Journal on Artificial Intelligence, Vol.4, No.1, pp. 27-35, 2022, DOI:10.32604/jai.2022.027839

Abstract Deep deterministic policy gradient (DDPG) has been proved to be effective in optimizing particle swarm optimization (PSO), but whether DDPG can optimize multi-objective discrete particle swarm optimization (MODPSO) remains to be determined. The present work aims to probe into this topic. Experiments showed that the DDPG can not only quickly improve the convergence speed of MODPSO, but also overcome the problem of local optimal solution that MODPSO may suffer. The research findings are of great significance for the theoretical research and application of MODPSO. More >

Jehangir Arshad¹, Ayesha Khan¹, Mariam Aftab¹, Mujtaba Hussain¹, Ateeq Ur Rehman², Shafiq Ahmad³, Adel M. Al-Shayea³, Muhammad Shafiq^4,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1153-1169, 2022, DOI:10.32604/cmc.2022.021917

Abstract Controlling feedback control systems in continuous action spaces has always been a challenging problem. Nevertheless, reinforcement learning is mainly an area of artificial intelligence (AI) because it has been used in process control for more than a decade. However, the existing algorithms are unable to provide satisfactory results. Therefore, this research uses a reinforcement learning (RL) algorithm to manage the control system. We propose an adaptive speed control of the motor system based on depth deterministic strategy gradient (DDPG). The actor-critic scenario using DDPG is implemented to build the RL agent. In addition, a framework has been created for traditional… More >