Year: 2022
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Gao, Fei, Geng, Peng, Guo, Jiaqi, Liu, Yuan, Guo, Dingfeng, Su, Yabo, Zhou, Jie, Wei, Xiao, Li, Jin, Liu, Xu
Institutions: Baidu (China), University of Cambridge
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4221140689