Explainable Reinforcement Learning: Enhancing Trust and Interpretability in Autonomous Systems
Abstract
Reinforcement learning (RL) has achieved remarkable success in robotics, gaming, and autonomous decision-making, yet its lack of transparency hinders widespread adoption. This paper explores techniques for making RL models more interpretable, including attention mechanisms, policy visualization, and human-in-the-loop learning. We introduce an explainable RL framework that provides real-time insights into decision-making processes while maintaining optimal performance. Case studies in self-driving cars, healthcare robotics, and financial trading illustrate the impact of explainability on user trust and safety. Challenges related to interpretability-performance trade-offs, generalization, and real-world validation are also discussed.
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