Interpreting and enhancing decisions in autonomous navigation: a belief-desire-intention reinforcement learning (BDI-RL) approach

Abstract

Explaining autonomy is becoming a crucial factor in the design of trustworthy autonomous platforms in both transport and smart living sectors. Interpretable reinforcement learning (RL) is an emerging research area that aims to explain why an autonomous platform adopts an action or set of actions. However, the state-of-the-art has focused on the design of explainable tools as independent modules that are not involved in the decision-making process of the RL agent. In this paper, we propose a novel belief-desire-intention RL (BDI-RL) approach that incorporates the explainable module as a belief model that enhances the learning capabilities of the RL as well as actions interpretability. To this end, we combine the merits of Dyna-Q algorithm as backbone RL model and belief maps as explainable element. The combined contribution of these models provides a robust model that emulates better the reasoning process of humans by leveraging beliefs and online agent-environment interactions. Simulations experiments are conducted in a grid environment of different sizes and obstacles. Comparisons are also provided to show the benefits of the proposed methodology.