Reinforcement learning based estimation of shortest paths in dynamically changing transportation networks

Finding the shortest path in a network is a classical problem, and a variety of search strategies have been proposed to solve it. In this paper, we review traditional approaches for finding shortest paths, namely, uninformed search, informed search and incremental search. The above traditional algorithms have been put to successful use for fixed networks with static link costs. However, in many practical contexts, such as transportation networks, the link costs can vary over time. We investigate the applicability of the aforementioned benchmark search strategies in a simulated transportation network where link costs (travel times) are dynamically estimated with vehicle mean speeds. As a comparison, we present performance metrics for a reinforcement learning based routing algorithm, which can interact with the network and learn the changing link costs through experience. Our results suggest that reinforcement learning algorithm computes optimal paths dynamically.