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An energy management strategy of deep reinforcement learning based on multi-agent architecture under self-generating conditions

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  • Chang, Chengcheng
  • Zhao, Wanzhong
  • Wang, Chunyan
  • Luan, Zhongkai

Abstract

To improve the driving efficiency of hybrid power vehicle, an energy management strategy of deep reinforcement learning based on multi-agent architecture under self-generating vehicle driving conditions is proposed. Firstly, the kinematics segments are self-generated based on the Wasserstein generative adversarial network. The generator network G is used to generate kinematics segments. The discriminator network D is used to judge the credibility of the generated kinematics segments with the Wasserstein distance. The speed distribution characteristics of the training conditions and verification conditions established based on the self-generated segments are verified. Afterward, a multi-agent algorithm based on twin delayed deep deterministic policy gradient algorithm for hybrid systems is proposed by introducing centralized training with decentralized execution framework. The engine and a motor are used as two independent agents respectively. Different reward functions are designed based on training objectives to establish a mutually beneficial relationship of cooperation-restraint between the two agents. A driving mode constraint is designed in the environment to improve sample utilization. Finally, the simulation results demonstrate that our method can achieve better performance compared with other existing works.

Suggested Citation

  • Chang, Chengcheng & Zhao, Wanzhong & Wang, Chunyan & Luan, Zhongkai, 2023. "An energy management strategy of deep reinforcement learning based on multi-agent architecture under self-generating conditions," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223019308
    DOI: 10.1016/j.energy.2023.128536
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    References listed on IDEAS

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    1. Murshed, Shabab & Nibir, Abu Shaikh & Razzaque, Md. Abdur & Roy, Palash & Elhendi, Ahmed Zohier & Hassan, Md. Rafiul & Hassan, Mohammad Mehedi, 2024. "Weighted fair energy transfer in a UAV network: A multi-agent deep reinforcement learning approach," Energy, Elsevier, vol. 292(C).

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