论文标题
基于手工握手的多代理学习的多模式混合动力汽车的能源管理
Energy Management of Multi-mode Hybrid Electric Vehicles based on Hand-shaking Multi-agent Learning
论文作者
论文摘要
未来的运输系统将是一个多代理网络，在该网络中，连接的AI代理可以共同努力解决我们这个时代的巨大挑战，例如缓解现实世界的驱动能源消耗。与现有的有关车辆能源管理的研究区分开来，该研究将多个输入和多个输出（MIMO）控制分解为单输出（MISO）控制，本文研究了一个多代理深度加固学习（MADRL）框架，以同时处理多个控制输出。通过引入独立比，为DRL代理提出了一种新的震动策略，并进行了参数研究以获得MADRL框架的最佳设置。该研究表明，独立比为0.2的MADRL是最好的，并且在常规的DRL框架上可以节省超过2.4％的能量。
The future transportation system will be a multi-agent network where connected AI agents can work together to address the grand challenges in our age, e.g., mitigation of real-world driving energy consumption. Distinguished from the existing research on vehicle energy management, which decoupled multiple inputs and multiple outputs (MIMO) control into single-output(MISO) control, this paper studied a multi-agent deep reinforcement learning (MADRL) framework to deal with multiple control outputs simultaneously. A new hand-shaking strategy is proposed for the DRL agents by introducing an independence ratio, and a parametric study is conducted to obtain the best setting for the MADRL framework. The study suggested that the MADRL with an independence ratio of 0.2 is the best, and more than 2.4% of energy can be saved over the conventional DRL framework.
