Electrical Engineering and Systems Science > Systems and Control
[Submitted on 4 Aug 2023 (v1), last revised 19 Feb 2024 (this version, v5)]
Title:Communication-Efficient Decentralized Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control
View PDF HTML (experimental)Abstract: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.
Submission history
From: Dong Chen [view email][v1] Fri, 4 Aug 2023 14:19:36 UTC (5,875 KB)
[v2] Tue, 8 Aug 2023 01:19:58 UTC (5,875 KB)
[v3] Tue, 12 Dec 2023 17:31:56 UTC (5,877 KB)
[v4] Thu, 21 Dec 2023 16:26:27 UTC (5,877 KB)
[v5] Mon, 19 Feb 2024 00:11:38 UTC (15,571 KB)
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