Holistic Deep-Reinforcement-Learning-based Training for Autonomous Navigation in Crowded Environments

In recent years, Deep Reinforcement Learning emerged as a promising approach for autonomous navigation of robots and has been utilized in various areas of navigation such as obstacle avoidance, motion planning, or decision making in crowded environments. 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 in crowded environments. 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 especially in situations with a high number of pedestrians.