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.
@article{arxiv.2201.12609,
title = {ApolloRL: a Reinforcement Learning Platform for Autonomous Driving},
author = {Fei Gao and Peng Geng and Jiaqi Guo and Yuan Liu and Dingfeng Guo and Yabo Su and Jie Zhou and Xiao Wei and Jin Li and Xu Liu},
journal= {arXiv preprint arXiv:2201.12609},
year = {2022}
}