Feature-Based Interpretable Reinforcement Learning based on State-Transition Models

Explanations can be generated from historical data alone: The method requires only a log of past interactions between an AI agent and its environment, making it practical to apply to existing systems without needing to modify how the agent was originally trained.

The approach works across different types of environments: Unlike some interpretability methods that only work in specific settings, this technique handles both continuous and discrete state-action spaces, increasing its potential applicability to diverse real-world RL systems.

Risk assessment is localized and actionable: Rather than explaining an entire AI model's behavior, the method provides "local explanations" focused on specific decisions, identifying which features most influence whether an action increases or decreases risk in that particular moment—information directly useful for human oversight.

Feature-based explanations are human-understandable: By translating agent decisions into terms of influential features (rather than abstract model parameters), the approach makes RL decision-making more transparent to non-technical stakeholders who need to evaluate whether an AI system is behaving responsibly.

Addresses a critical gap in RL transparency: While explainability has become important for supervised AI systems, reinforcement learning—which makes sequential decisions in dynamic environments—has received less attention; this work directly tackles the challenge of explaining RL agent behavior in real-world operational contexts.