How can reinforcement learning be applied to transportation?

Reinforcement Learning (RL), a field of machine learning, is based on the principle of trial and error. In easier words, it learns from its own mistakes and corrects the mistake. The aim is simply to build a strategy to guide the intelligent agent to take action in a sequence that leads to fulfilling some ultimate goal. Autonomous Driving (AD) uses Deep Reinforcement Learning (DRL) to make real-time decisions and strategies, not only in AD but also in the field of sales, management and many others. In this article, we will mainly discuss how RL can be used in transportation for better intelligent solutions.