  Despite promising progress in reinforcement learning (RL), developing
algorithms for autonomous driving (AD) remains challenging: one of the critical
issues being the absence of an open-source platform capable of training and
effectively validating the RL policies on real-world data. We propose
DriverGym, an open-source OpenAI Gym-compatible environment specifically
tailored for developing RL algorithms for autonomous driving. DriverGym
provides access to more than 1000 hours of expert logged data and also supports
reactive and data-driven agent behavior. The performance of an RL policy can be
easily validated on real-world data using our extensive and flexible
closed-loop evaluation protocol. In this work, we also provide behavior cloning
baselines using supervised learning and RL, trained in DriverGym. We make
DriverGym code, as well as all the baselines publicly available to further
stimulate development from the community.
