  This study addresses a gap in the utilization of Reinforcement Learning (RL)
and Machine Learning (ML) techniques in solving the Stochastic Vehicle Routing
Problem (SVRP) that involves the challenging task of optimizing vehicle routes
under uncertain conditions. We propose a novel end-to-end framework that
comprehensively addresses the key sources of stochasticity in SVRP and utilizes
an RL agent with a simple yet effective architecture and a tailored training
method. Through comparative analysis, our proposed model demonstrates superior
performance compared to a widely adopted state-of-the-art metaheuristic,
achieving a significant 3.43% reduction in travel costs. Furthermore, the model
exhibits robustness across diverse SVRP settings, highlighting its adaptability
and ability to learn optimal routing strategies in varying environments. The
publicly available implementation of our framework serves as a valuable
resource for future research endeavors aimed at advancing RL-based solutions
for SVRP.
