  We introduce ApolloRL, an open platform for research in reinforcement
learning for autonomous driving. The platform provides a complete closed-loop
pipeline with training, simulation, and evaluation components. It comes with
300 hours of real-world data in driving scenarios and popular baselines such as
Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) agents. We
elaborate in this paper on the architecture and the environment defined in the
platform. In addition, we discuss the performance of the baseline agents in the
ApolloRL environment.
