Communication-Aware Reinforcement Learning for Cooperative Adaptive Cruise Control
This addresses a critical bottleneck for connected autonomous vehicles in improving traffic efficiency and safety, though it appears incremental as an enhancement to existing MARL methods.
The paper tackles scalability issues in Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control when vehicles dynamically join or leave platoons, proposing Communication-Aware Reinforcement Learning which significantly outperforms baselines in scalability, robustness, and system performance across various traffic scenarios.
Cooperative Adaptive Cruise Control (CACC) plays a pivotal role in enhancing traffic efficiency and safety in Connected and Autonomous Vehicles (CAVs). Reinforcement Learning (RL) has proven effective in optimizing complex decision-making processes in CACC, leading to improved system performance and adaptability. Among RL approaches, Multi-Agent Reinforcement Learning (MARL) has shown remarkable potential by enabling coordinated actions among multiple CAVs through Centralized Training with Decentralized Execution (CTDE). However, MARL often faces scalability issues, particularly when CACC vehicles suddenly join or leave the platoon, resulting in performance degradation. To address these challenges, we propose Communication-Aware Reinforcement Learning (CA-RL). CA-RL includes a communication-aware module that extracts and compresses vehicle communication information through forward and backward information transmission modules. This enables efficient cyclic information propagation within the CACC traffic flow, ensuring policy consistency and mitigating the scalability problems of MARL in CACC. Experimental results demonstrate that CA-RL significantly outperforms baseline methods in various traffic scenarios, achieving superior scalability, robustness, and overall system performance while maintaining reliable performance despite changes in the number of participating vehicles.