GPG-HT: Generalized Policy Gradient with History-Aware Decision Transformer for Probabilistic Path Planning
This addresses congestion in urban traffic planning by providing more accurate probabilistic path planning, though it appears incremental as it combines existing techniques.
The paper tackles the reliable shortest path problem in stochastic transportation networks by integrating a decision Transformer with Generalized Policy Gradient, improving accuracy and stability of path decisions. Experimental results on the Sioux Falls Network show it outperforms previous baselines in on-time arrival probability.
With the rapidly increased number of vehicles in urban areas, existing road infrastructure struggles to accommodate modern traffic demands, resulting in the issue of congestion. This highlights the importance of efficient path planning strategies. However, most recent navigation models focus solely on deterministic or time-dependent networks, while overlooking the correlations and the stochastic nature of traffic flows. In this work, we address the reliable shortest path problem within stochastic transportation networks under certain dependencies. We propose a path planning solution that integrates the decision Transformer with the Generalized Policy Gradient (GPG) framework. Based on the decision Transformer's capability to model long-term dependencies, our proposed solution improves the accuracy and stability of path decisions. Experimental results on the Sioux Falls Network (SFN) demonstrate that our approach outperforms previous baselines in terms of on-time arrival probability, providing more accurate path planning solutions.