Reinforcement learning (RL) is being more extensively applied in autonomous navigation systems to make decisions in real time in dynamic settings. Nevertheless, RL models have a black-box character that makes it difficult to validate safety and acceptability by regulations. The presented paper suggests a combined explainable RL (XRL) system combining explainable policy structures, sophisticated post hoc explanatory algorithms (such as saliency maps, counterfactuals and natural language explanations), and effective human-in-the-loop feedback structures. It is strictly tested on urban driving and warehouse robotics and shows a 94.7% success rate of navigation and a 17.8% decrease in collision rates over non-explainable baselines. User research suggests 28–32 percent increase in trust among the experts and lay users. It is interesting to note that multi-modal explanations facilitate comprehensive diagnostics, facilitate intuitive knowledge to various stakeholders without undermining operational effectiveness. Although issues of real-time latency of computations and scalability of human feedback exist, the outlined practice will be a significant milestone towards safe, transparent, and robust RL-powered autonomous systems. This breakthrough helps to make the wider society more accepting and adherent to ethical and regulatory norms, which preconditions the confident and credible application of AI-based navigation apps.