We propose a geometric reinforcement learning algorithm for real-time path planning for mobile sensor networks (MSNs) in the problem of reconstructing a spatial-temporal varying field described by the advection-diffusion partial differential equation. A Luenberger state estimator is provided to reconstruct the concentration field, which uses the collected measurements from a MSN along its trajectory. Since the path of the MSN is critical in reconstructing the field, a novel geometric reinforcement learning (GRL) algorithm is developed for the real-time path planning. The basic idea of GRL is to divide the whole area into a series of lattice to employ a specific time-varying reward matrix, which contains the information of the length of path and the mapping error. Thus, the proposed GRL can balance the performance of the field reconstruction and the efficiency of the path. By updating the reward matrix, the real-time path planning problem can be converted to the shortest path problem in a weighted graph, which can be solved efficiently using dynamic programming. The convergence of calculating the reward matrix is theoretically proven. Simulation results serve to demonstrate the effectiveness and feasibility of the proposed GRL for a MSN.