In recent years, Deep Reinforcement Learning emerged as a promising approach for autonomous navigation of ground vehicles and has been utilized in various areas of navigation such as cruise control, lane changing, or obstacle avoidance. However, most research works either focus on providing an end-to-end solution training the whole system using Deep Reinforcement Learning or focus on one specific aspect such as local motion planning. This however, comes along with a number of problems such as catastrophic forgetfulness, inefficient navigation behavior, and non-optimal synchronization between different entities of the navigation stack. In this paper, we propose a holistic Deep Reinforcement Learning training approach in which the training procedure is involving all entities of the navigation stack. This should enhance the synchronization between- and understanding of all entities of the navigation stack and as a result, improve navigational performance. We trained several agents with a number of different observation spaces to study the impact of different input on the navigation behavior of the agent. In profound evaluations against multiple learning-based and classic model-based navigation approaches, our proposed agent could outperform the baselines in terms of efficiency and safety attaining shorter path lengths, less roundabout paths, and less collisions.

翻译：近年来，深度强化学习已成为地面车辆自主导航的一种有前景的方法，并被应用于巡航控制、车道变更或障碍物规避等导航领域。然而，大多数研究工作要么侧重于提供端到端解决方案，使用深度强化学习训练整个系统，要么聚焦于局部运动规划等特定方面。但这会带来一系列问题，如灾难性遗忘、低效导航行为以及导航堆栈不同实体间的非最优同步。本文提出一种整体深度强化学习训练方法，其训练过程涉及导航堆栈的所有实体。这应能增强导航堆栈所有实体间的同步与理解，从而提升导航性能。我们训练了多个具有不同观测空间的智能体，以研究不同输入对智能体导航行为的影响。在与多种基于学习和基于经典模型的导航方法进行深入评估后，我们提出的智能体在效率和安全性方面优于基准模型，实现了更短的路径长度、更少的迂回路径以及更少的碰撞。