  This paper proposes a cooperative strategy of connected and automated
vehicles (CAVs) longitudinal control for partially connected and automated
traffic environment based on deep reinforcement learning (DRL) algorithm, which
enhances the string stability of mixed traffic, car following efficiency, and
energy efficiency. Since the sequences of mixed traffic are combinatory, to
reduce the training dimension and alleviate communication burdens, we
decomposed mixed traffic into multiple subsystems where each subsystem is
comprised of human-driven vehicles (HDV) followed by cooperative CAVs. Based on
that, a cooperative CAV control strategy is developed based on a deep
reinforcement learning algorithm, enabling CAVs to learn the leading HDV's
characteristics and make longitudinal control decisions cooperatively to
improve the performance of each subsystem locally and consequently enhance
performance for the whole mixed traffic flow. For training, a distributed
proximal policy optimization is applied to ensure the training convergence of
the proposed DRL. To verify the effectiveness of the proposed method, simulated
experiments are conducted, which shows the performance of our proposed model
has a great generalization capability of dampening oscillations, fulfilling the
car following and energy-saving tasks efficiently under different penetration
rates and various leading HDVs behaviors.
  Keywords: partially connected automated traffic environment, cooperative
control, deep reinforcement learning, traffic oscillation dampening, energy
efficiency
