论文标题
Apollorl：一个用于自动驾驶的加固学习平台
ApolloRL: a Reinforcement Learning Platform for Autonomous Driving
论文作者
论文摘要
我们介绍了Apollorl，这是一个开放的研究平台，用于研究自动驾驶的增强学习。该平台提供了一条完整的闭环管道，并提供培训，仿真和评估组件。它在驾驶场景中带有300个小时的现实数据，以及诸如近端策略优化（PPO）和软演员批评（SAC）代理等流行基线。我们在本文中详细介绍了平台中定义的架构和环境。此外，我们讨论了基线代理在Apollorl环境中的性能。
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.
