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Autonomous navigation of mobile robots is an essential aspect in use casessuch as delivery, assistance or logistics. Although traditional planningmethods are well integrated into existing navigation systems, they struggle inhighly dynamic environments. On the other hand,Deep-Reinforcement-Learning-based methods show superior performance in dynamicobstacle avoidance but are not suitable for long-range navigation and strugglewith local minima. In this paper, we propose aDeep-Reinforcement-Learning-based control switch, which has the ability toselect between different planning paradigms based solely on sensor dataobservations. Therefore, we develop an interface to efficiently operatemultiple model-based, as well as learning-based local planners and integrate avariety of state-of-the-art planners to be selected by the control switch.Subsequently, we evaluate our approach against each planner individually andfound improvements in navigation performance especially for highly dynamicscenarios. Our planner was able to prefer learning-based approaches insituations with a high number of obstacles while relying on the traditionalmodel-based planners in long corridors or empty spaces.

All-in-One: A DRL-based Control Switch Combining State-of-the-art Navigation Planners