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, 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.Comment: 11 pages, 7 figure