Multi-Agent Reinforcement Learning for Urban Crowd Sensing with For-Hire Vehicles

Recently, vehicular crowd sensing (VCS) that leverages sensor-equipped urban vehicles to collect city-scale sensory data has emerged as a promising paradigm for urban sensing. Nowadays, a wide spectrum of VCS tasks are carried out by for-hire vehicles (FHVs) due to various hardware and software constraints that are difficult for private vehicles to satisfy. However, such FHV-enabled VCS systems face a fundamental yet unsolved problem of striking a balance between the order-serving and sensing outcomes. To address this problem, we propose a novel graph convolutional cooperative multi-agent reinforcement learning (GCC-MARL) framework, which helps FHVs make distributed routing decisions that cooperatively optimize the system-wide global objective. Specifically, GCC-MARL meticulously assigns credits to agents in the training process to effectively stimulate cooperation, represents agents' actions by a carefully chosen statistics to cope with the variable agent scales, and integrates graph convolution to capture useful spatial features from complex large-scale urban road networks. We conduct extensive experiments with a real-world dataset collected in Shenzhen, China, containing around 1 million trajectories and 50 thousand orders of 553 taxis per-day from June 1st to 30th, 2017. Our experiment results show that GCC-MARL outperforms state-of-the-art baseline methods in order-serving revenue, as well as sensing coverage and quality.