Explainability in Deep Reinforcement Learning

Reinforcement Learning remains largely unexplained: While explainability techniques have been extensively developed for image classification and other supervised learning tasks, understanding how reinforcement learning agents make decisions has received significantly less attention, leaving a major gap in responsible AI deployment.

Two main approaches exist for explaining RL systems: The paper identifies transparent algorithms (designed to be interpretable from the start) and post-hoc explainability (explaining decisions after the fact) as the primary methods for making RL agents understandable, each with different tradeoffs for practical use.

Explainability is critical for high-stakes applications: In situations where RL systems must justify their behavior—such as healthcare, autonomous vehicles, or criminal justice—better explainability could help ensure these "black box" systems are ethical, trustworthy, and safe enough for public deployment.

Current XAI techniques may not transfer directly to RL: The paper emphasizes that explainability methods developed for other AI tasks cannot simply be applied to reinforcement learning, which requires specialized approaches tailored to how RL agents learn and make sequential decisions.

Responsible AI requires diverse audiences to understand AI decisions: The paper stresses that explainable RL must serve not just technical experts but also policymakers, end-users, and the general public, making accessibility and clarity essential for trustworthy AI systems in everyday applications.