Generalized Policy Gradient with History-Aware Decision Transformer for Path Planning

With the rapidly increased number of vehicles in urban areas, existing road infrastructure struggles to accommodate modern traffic demands, resulting in congestion. This highlights the importance of efficient path planning strategies. Most recent navigation models focus on deterministic or time-dependent networks, overlooking correlations and the stochastic nature of traffic flows. In this work, we address the reliable shortest path problem in stochastic transportation networks and propose a path planning solution integrating the decision Transformer with the Generalized Policy Gradient (GPG) framework. Leveraging the Transformer's ability to model long-term dependencies, our solution improves path decision accuracy and stability. Experiments on the Sioux Falls (SFN) and large Anaheim (AN) networks show consistent improvement in on-time arrival probabilities by capturing non-Markovian dependencies in historical routing decisions on real-world topologies.