CODEX: A Cluster-Based Method for Explainable Reinforcement Learning

RL systems lack transparency for high-stakes decisions: The paper identifies a critical barrier to deploying reinforcement learning in real-world applications—current RL algorithms cannot adequately explain their decision-making processes, which undermines user trust in high-risk scenarios where accountability matters.

CODEX uses clustering to make RL behavior interpretable: The method organizes an RL agent's actions into semantic clusters that summarize its behavior patterns, making it possible to understand why an agent takes specific actions rather than treating it as a "black box."

The approach captures both timing and context: The research demonstrates that semantic clusters preserve important information about when actions occur (temporal information) and what entities are involved, suggesting the explanations reflect meaningful patterns rather than arbitrary groupings.

Key moments can be identified automatically: By analyzing the agent's internal representations, CODEX can pinpoint the most important decision points in an agent's behavior sequence, helping humans focus on critical moments rather than reviewing every action.

Explainability techniques from language AI can help RL: The work shows that methods originally developed for natural language processing—specifically clustering approaches—can be adapted to make reinforcement learning systems more transparent and trustworthy for real-world deployment.