Automatically Generated Curriculum based Reinforcement Learning for Autonomous Vehicles in Urban Environment

Abstract

We address the problem of learning autonomous driving behaviors in urban intersections using deep reinforcement learning (DRL). DRL has become a popular choice for creating autonomous agents due to its success in various tasks. However, as the problems tackled become more complex, the number of training iterations necessary increase drastically. Curriculum learning has been shown to reduce the required training time and improve the performance of the agent, but creating an optimal curriculum often requires human handcrafting. In this work, we learn a policy for urban intersection crossing using DRL and introduce a method to automatically generate the curriculum for the training process from a candidate set of tasks. We compare the performance of the automatically generated curriculum (AGC) training to those of randomly generated sequences and show that AGC can significantly reduce the training time while achieving similar or better performance.