Interpretable Decision Making in Reinforcement Learning

Thesis: Interpretable RL systems require automated concept selection mechanisms that identify human-understandable factors that distinguish optimal decision boundaries.

Overview

The fundamental challenge in interpretable reinforcement learning lies in bridging the gap between human understanding and optimal decision-making. While traditional RL systems optimize for performance through opaque policies, truly interpretable systems must make their decision boundaries transparent without sacrificing optimality. This requires sophisticated mechanisms that automatically identify which human-understandable concepts actually matter for distinguishing between different optimal actions.

The breakthrough insight is that interpretability in RL is not just about using human-readable features, but about selecting features that preserve the decision structure of the original problem. Decision-Relevant Concepts provide this bridge by ensuring that states sharing identical concept representations must also share identical optimal actions. This creates interpretable decision boundaries that align with algorithmic optimality rather than just human intuition.

How the Concepts Connect

Concept-Based Models serve as the architectural foundation, creating a bottleneck between raw observations and policy outputs through interpretable concept representations. However, the critical innovation comes from automated Concept Selection mechanisms that determine which concepts to include in this bottleneck.

The theoretical connection flows through State Abstraction theory, which provides the mathematical framework for understanding when different states can be treated equivalently. When concepts create state abstractions, the resulting Abstraction Error directly bounds the performance loss of concept-based policies. This creates a principled optimization objective: minimize abstraction error while maintaining interpretability.

The Decision-Relevant Selection algorithm operationalizes this theory by formulating concept selection as a Mixed Integer Linear Programming problem. It uses Q-Distance metrics to identify which states require different optimal actions, then selects concepts that preserve these critical distinctions. States that need different actions must have different concept representations, while states requiring the same action can safely share concept values.

This automated approach addresses the scaling challenges of manual concept curation. Rather than relying on domain experts to guess which features matter for decisions, the system automatically discovers Decision-Relevant Concepts that provably preserve optimal behavior. The resulting policies maintain interpretability while achieving theoretical performance guarantees of V^π*(s) - V^π_c*(s) ≤ 2ε(g_c)/(1-γ)².

The practical benefits emerge through Test-Time Intervention, where humans can effectively correct concept predictions during deployment. Well-selected concepts improve intervention effectiveness by 40-87% because human corrections target features that actually influence optimal decisions rather than arbitrary correlates.

Implications

This connection reveals that interpretability and optimality are not fundamentally at odds—they can be aligned through principled concept selection. The key insight is that human-understandable features become interpretable decisions only when they preserve the underlying decision structure.

For System Design: Interpretable Reinforcement Learning systems should prioritize decision-relevance over feature comprehensibility. A concept that humans easily understand but doesn't distinguish between different optimal actions provides false interpretability that misleads rather than enlightens.

For Human-AI Interaction: Effective Human-AI Interaction in RL requires that human interventions target decision-relevant concepts. The 40-87% improvement in intervention effectiveness demonstrates that the same human effort becomes dramatically more valuable when applied to properly selected concepts.

For Theoretical Understanding: The mathematical connection between State Abstraction and Concept-Based Models provides the first rigorous framework for automated interpretability with performance guarantees. This moves interpretable RL from an art of manual feature engineering to a science with provable properties.

For Practical Deployment: Automated Concept Selection enables interpretable RL to scale beyond domains where expert knowledge is readily available. The system can discover decision-relevant concepts in new environments without requiring domain-specific concept banks or manual curation.

The broader implication is that interpretability is not a constraint on performance but a design principle that, when properly implemented through decision-relevant concept selection, can maintain optimality while enabling human understanding and effective intervention.

Related Concepts