Explainable Reinforcement Learning for Autonomous Robotic Decision Making

Abstract

Autonomous robotic systems are increasingly deployed in industrial automation, healthcare, logistics, agriculture, defense, and intelligent transportation, where they must make complex decisions in dynamic environments. Reinforcement Learning (RL) enables robots to learn optimal actions through interaction with their environment, but most deep RL models function as black boxes, limiting transparency and trust in safety-critical applications. This paper proposes an Explainable Reinforcement Learning for Autonomous Robotic Decision Making (XORL) framework that integrates reinforcement learning with Explainable AI (XAI) to improve decision interpretability. The framework combines multimodal sensor data, policy optimization, confidence estimation, reward decomposition, policy visualization, and decision traceability to generate understandable explanations for robotic actions. It evaluates performance using metrics such as navigation success, obstacle avoidance, learning stability, computational efficiency, explanation consistency, and reliability. Experimental results demonstrate that XORL enhances decision transparency, operator trust, safety awareness, and autonomous task performance while maintaining competitive learning efficiency, supporting the development of trustworthy and human-centric autonomous robotic systems.