论文标题
自主驾驶的深度加固学习：一项调查
Deep Reinforcement Learning for Autonomous Driving: A Survey
论文作者
论文摘要
随着深度表示学习的发展，增强学习的领域（RL）已成为一个强大的学习框架，现在能够在高维环境中学习复杂的政策。这篇综述总结了深入的强化学习（DRL）算法，并提供了自动驾驶任务的分类学，其中（d）RL方法已采用，同时解决了自动驾驶剂现实世界部署的关键计算挑战。它还描绘了相邻的域，例如行为克隆，模仿学习，相关的逆增强学习，但不是经典的RL算法。讨论了模拟器在培训代理中的作用，验证，测试和鲁棒性RL中现有解决方案的作用。
With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in real world deployment of autonomous driving agents. It also delineates adjacent domains such as behavior cloning, imitation learning, inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test and robustify existing solutions in RL are discussed.
