Using Semantic Information to Improve Generalization of Reinforcement Learning Policies for Autonomous Driving

Abstract

The problem of generalization of reinforcement learning policies to new environments is seldom addressed but essential in practical applications. We focus on this problem in an autonomous driving context using the CARLA simulator and first show that semantic information is the key to a good generalization for this task. We then explore and compare different ways to exploit semantic information at training time in order to improve generalization in an unseen environment without fine-tuning, showing that using semantic segmentation as an auxiliary task is the most efficient approach.