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ApolloRL: a Reinforcement Learning Platform for Autonomous Driving

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arxiv 2201.12609 v1 pith:F4I5ZO42 submitted 2022-01-29 cs.RO cs.LGstat.ML

Fei Gao , Peng Geng , Jiaqi Guo , Yuan Liu , Dingfeng Guo , Yabo Su , Jie Zhou , Xiao Wei
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Jin Li Xu Liu
This is my paper
classification cs.ROcs.LGstat.ML
keywords platformapollorldrivingagentsautonomousenvironmentlearningreinforcement
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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