  Large-scale spatial data such as air quality, thermal conditions and location
signatures play a vital role in a variety of applications. Collecting such data
manually can be tedious and labour intensive. With the advancement of robotic
technologies, it is feasible to automate such tasks using mobile robots with
sensing and navigation capabilities. However, due to limited battery lifetime
and scarcity of charging stations, it is important to plan paths for the robots
that maximize the utility of data collection, also known as the informative
path planning (IPP) problem. In this paper, we propose a novel IPP algorithm
using reinforcement learning (RL). A constrained exploration and exploitation
strategy is designed to address the unique challenges of IPP, and is shown to
have fast convergence and better optimality than a classical reinforcement
learning approach. Extensive experiments using real-world measurement data
demonstrate that the proposed algorithm outperforms state-of-the-art algorithms
in most test cases. Interestingly, unlike existing solutions that have to be
re-executed when any input parameter changes, our RL-based solution allows a
degree of transferability across different problem instances.
