Vehicle Routing Problem Solving Using Reinforcement Learning

In this study, the Vehicle Routing Problem (VRP) is solved using reinforcement learning (RL) approaches. In order to service a group of consumers efficiently, the VRP involves identifying the shortest, most cost-effective, and fastest routes for a fleet of vehicles. With large-scale instances and dynamic situations, traditional methods for solving the VRP confront difficulties. A promising solution to this issue is provided by RL, a type of artificial intelligence. The suggested RL-based VRP solver learns to make wise routing decisions based on observable environmental information by utilizing RL algorithms such as deep Q-networks (DQN) or proximal policy optimization (PPO). Experimental results show that the RL-based technique is effective, producing competitive or better solutions than conventional approaches, especially for complicated VRP situations.