Wireless Multi-Hop Networks (WMNs) have been widely applied in many industrial monitoring applications, where typically nodes deployed on machines sample the data periodically and the data then needs to be gathered to the sink node in time. Due to the complicated industrial environment, the channel capacity between neighboring nodes differs greatly from each other. This leads to a typical dilemma of radio communication module selection, i.e., choosing the wide-bandwidth module with short communication range or the long-range module with low bandwidth. Guaranteeing both the connectivity of the network and high network throughput is not easy. To address this issue, we equip the nodes with heterogeneous radio modules, leveraging their advantages to ensure both long-distance and high-bandwidth communication capabilities. The problem of efficiently scheduling the links between the nodes with multiple heterogeneous RF modules to minimize the data gathering delay has not yet been explored in existing research. In this paper, we propose a low-latency data collection method based on Deep Reinforcement Learning, with which nodes can autonomously appropriately schedule and assign their heterogeneous RF modules to their transmission/reception targets based on the current amount of their local data as well as that of their neighbors’ data. Extensive simulations are conducted, and the results show that the proposed method can achieve 10%-25% latency reduction compared to existing multi-radio approaches.