Evaluating robustness of deep reinforcement learning for autonomous driving

Abstract

Deep reinforcement learning has been a topic of research in recent years and has been expanding into the domain of autonomous driving. As autonomous driving is likely to involve people, such as daily commuters, it is necessary to ensure the machine will perform well enough in real-life environments not to put anyone at risk. There exist possible approaches to make the transition from a simulation to real life easier, such as domain randomization. This paper uses OpenAI's CarRacing-v2 environment and the CARLA simulator to investigate the effect of domain randomization on training efficiency and robustness for a Deep Q-Network algorithm for autonomous driving. The results show a decrease in training efficiency and higher variance during training for both environments. CARLA also indicates an overestimation during training. As for robustness testing, while visual domain randomization in CarRacing-v2 does not suggest a significant influence on robustness, the dynamic domain randomization in CARLA offers a positive influence toward robustness at the expense of some reward.