  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.
