abstract
- © 2020, Springer-Verlag France SAS, part of Springer Nature.Abstract: Explainable outcomes in autonomous navigation have become crucial for drivers, other vehicles, as well as for pedestrians. Creating trustworthy strategies is mandatory for the integration of self-driving cars into quotidian environments. This paper presents the successful implementation of an explainable Fuzzy Deep Reinforcement Learning approach for autonomous vehicles based on the AWS DeepRacerTM platform. A model of the environment is created by transforming crisp values into linguistic variables. A fuzzy inference system is used to define the reward of the vehicle depending on its current state. Guidelines to define the actions and to improve performance of the reinforcement learning agent are given based on the characteristics of the existing hardware. The performance of the models is tested on tracks with distinctive properties using agents with different policies and action spaces, and shows explainable and successful navigation of the agent on diverse scenarios. Graphic Abstract: [Figure not available: see fulltext.].