ApolloRL: a Reinforcement Learning Platform for Autonomous Driving

2022-01-29 · Fei Gao, Peng Geng, Jiaqi Guo, YuAn Liu, Dingfeng Guo, Yabo Su, Jie zhou, Xiao Wei, Jin Li, Xu Liu

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.

📄 PDF Abstract BibTeX arXiv:2201.12609

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Autonomous Driving reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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