  Connected and autonomous vehicles (CAVs) promise next-gen transportation
systems with enhanced safety, energy efficiency, and sustainability. One
typical control strategy for CAVs is the so-called cooperative adaptive cruise
control (CACC) where vehicles drive in platoons and cooperate to achieve safe
and efficient transportation. In this study, we formulate CACC as a multi-agent
reinforcement learning (MARL) problem. Diverging from existing MARL methods
that use centralized training and decentralized execution which require not
only a centralized communication mechanism but also dense inter-agent
communication during training and online adaptation, we propose a fully
decentralized MARL framework for enhanced efficiency and scalability. In
addition, a quantization-based communication scheme is proposed to reduce the
communication overhead without significantly degrading the control performance.
This is achieved by employing randomized rounding numbers to quantize each
piece of communicated information and only communicating non-zero components
after quantization. Extensive experimentation in two distinct CACC settings
reveals that the proposed MARL framework consistently achieves superior
performance over several contemporary benchmarks in terms of both communication
efficiency and control efficacy. In the appendix, we show that our proposed
framework's applicability extends beyond CACC, showing promise for broader
intelligent transportation systems with intricate action and state spaces.
