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Vehicle Routing Problem (VRP) is a well-known NP-hard combinatorial optimization problem at the heart of the transportation and logistics research. VRP can be exactly solved only for small instances of the problem with conventional methods. Traditionally this problem has been solved using heuristic methods for large instances even though there is no guarantee of optimality. Efficient solution adopted to VRP may lead to significant savings per year in large transportation and logistics systems. Much of the recent works using Reinforcement Learning are computationally intensive and face the three curse of dimensionality: explosions in state and action spaces and high stochasticity i.e., large number of possible next states for a given state action pair. Also, recent works on VRP don't consider the realistic simulation settings of customer environments, stochastic elements and scalability aspects as they use only …