ApolloRL：自動運転のための強化学習プラットフォーム
ApolloRL: a Reinforcement Learning Platform for Autonomous Driving
訓練・シミュレーション・評価を統合した閉ループ型の自動運転強化学習プラットフォームを提案し、300時間の実走行データとPPO/SACのベースラインを提供する。
著者: Fei Gao, Peng Geng, Jiaqi Guo, Yuan Liu, Dingfeng Guo, Yabo Su, Jie Zhou, Xiao Wei, Jin Li, Xu Liu
分類: cs.RO, cs.LG, stat.ML
原文アブストラクト
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.
