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Paper detail

ApolloRL: a Reinforcement Learning Platform for Autonomous Driving

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.

preprint2022arXivOpen access
Fei GaoPeng GengJiaqi GuoYuan LiuDingfeng GuoYabo SuJie ZhouXiao WeiJin LiXu Liu
Machine LearningRobotics
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Fei GaoPeng GengJiaqi GuoYuan LiuDingfeng GuoYabo SuJie ZhouXiao WeiJin LiXu Liu

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