Since deep neural networks' resurgence, reinforcement learning has gradually strengthened and surpassed humans in many conventional games. However, it is not easy to copy these accomplishments to autonomous driving because state spaces are immensely complicated in the real world and action spaces are continuous and fine control is necessary. Besides, autonomous driving systems must also maintain their functionality regardless of the environment's complexity. The deep reinforcement learning domain (DRL) has become a robust learning framework to handle complex policies in high dimensional surroundings with deep representation learning. This research outlines deep, reinforcement learning algorithms (DRL). It presents a nomenclature of autonomous driving in which DRL techniques have been used, thus discussing important computational issues in evaluating autonomous driving agents in the real environment. Instead, it involves similar but not standard RL techniques, adjoining fields such as emulation of actions, modelling imitation, inverse reinforcement learning. The simulators' role in training agents is addressed, as are the methods for validating, checking and robustness of existing RL solutions.
翻译:自深层神经网络死灰复燃以来,强化学习在许多常规游戏中逐渐加强并超越了人类。然而,由于国家空间在现实世界中极其复杂,行动空间是连续的,并且需要精细的控制,因此不容易将这些成就复制为自主驾驶。此外,自主驾驶系统也必须保持其功能,而不管环境的复杂性如何。深层强化学习领域(DRL)已成为一个强有力的学习框架,以处理具有深层代表性学习的高层次周围的复杂政策。这项研究概述了深度强化学习算法(DRL),它展示了使用DRL技术的自主驾驶术语,从而讨论了在现实环境中评价自主驾驶器的重要计算问题。相反,它涉及类似但非标准的RL技术,同时涉及模拟行动、模拟、反向强化学习等领域。模拟者在培训代理人中的作用得到了解决,以及现有RL解决方案的验证、检查和稳健的方法。