Quantifying Transferability of Deep Reinforcement Learning Navigation Algorithms Using Scene Similarity Metrics
The core message of this paper is to propose a novel scene similarity metric to quantify the transferability of deep reinforcement learning (DRL) navigation algorithms between training and test scenes. The authors also design a robust DRL navigation algorithm using a fused local map as the observation to improve the transferability.