  Cooperative adaptive cruise control (CACC) has been recognized as a
fundamental function of autonomous driving, in which platoon stability and
energy efficiency are outstanding challenges that are difficult to accommodate
in real-world operations. This paper studied the CACC of connected and
autonomous vehicles (CAVs) based on the multi-agent reinforcement learning
algorithm (MARL) to optimize platoon stability and energy efficiency
simultaneously. The optimal use of communication bandwidth is the key to
guaranteeing learning performance in real-world driving, and thus this paper
proposes a communication-efficient MARL by incorporating the quantified
stochastic gradient descent (QSGD) and a binary differential consensus (BDC)
method into a fully-decentralized MARL framework. We benchmarked the
performance of our proposed BDC-MARL algorithm against several several
non-communicative andcommunicative MARL algorithms, e.g., IA2C, FPrint, and
DIAL, through the evaluation of platoon stability, fuel economy, and driving
comfort. Our results show that BDC-MARL achieved the highest energy savings,
improving by up to 5.8%, with an average velocity of 15.26 m/s and an
inter-vehicle spacing of 20.76 m. In addition, we conducted different
information-sharing analyses to assess communication efficacy, along with
sensitivity analyses and scalability tests with varying platoon sizes. The
practical effectiveness of our approach is further demonstrated using
real-world scenarios sourced from open-sourced OpenACC.
