Reinforcement Learning Interpretability

Summary: A field focused on making reinforcement learning agents explainable and understandable to humans. This encompasses methods for creating interpretable policies, providing explanations for agent decisions, and enabling human oversight and intervention in RL systems.

Overview

Reinforcement Learning Interpretability addresses the black-box nature of traditional RL agents by developing methods that make their decision-making processes transparent and comprehensible. The field is motivated by the need for trust, safety, and human collaboration in high-stakes domains where RL agents operate.

The core challenge is balancing interpretability with performance - simpler, more interpretable models may sacrifice the complexity needed for optimal decision-making. Key approaches include:

• Concept-based methods that decompose decisions through human-understandable concepts
• Policy visualization techniques that reveal learned strategies
• Causal explanations that identify which factors drive specific actions
• Interactive frameworks that enable human oversight and correction

A critical insight from recent work is that interpretability should be grounded in decision-relevance - explanatory concepts should distinguish between states that require different optimal actions, rather than simply being human-interpretable in isolation.

Key Details

Core Principles:

• Interpretable concepts must be decision-relevant to preserve optimal policy structure
• Manual concept selection is costly, requires domain expertise, and provides no performance guarantees
• Automatic concept selection can match or exceed manually curated approaches
• Well-selected concepts improve the effectiveness of human interventions during deployment

Technical Approaches:

• Concept-Based Models using concept predictors followed by concept-to-action policies
• State Abstractions theory to formalize when concepts preserve decision structure
• Optimization methods like Mixed Integer Linear Programming for concept selection
• Performance bounds relating abstraction error to policy value loss (≤ 2ε/(1-γ)²)

Challenges:

• Concept selection is NP-hard but tractable due to environmental constraints
• Imperfect concept predictors require probabilistic separation constraints
• Balancing interpretability with performance across diverse domains

Relationships

• Concept-Based Models — primary architectural approach for interpretable RL policies
• Decision-Relevant Concepts — key principle for selecting meaningful explanatory features
• State Abstractions — theoretical foundation connecting interpretability to optimal decision preservation
• Test-Time Intervention — mechanism for human oversight and correction of agent decisions
• Explainable AI — broader field encompassing interpretability across ML domains
• Human-AI Collaboration — application area where interpretable RL enables effective human-agent teamwork
• Feature Selection — related optimization problem for identifying relevant input variables
• Concept Bottleneck Models — interpretable architecture from supervised learning adapted to RL

Sources

• sources/selecting-decision-relevant-concepts-in-reinforcement-learning — foundational work on automatic concept selection using decision-relevance principle, with theoretical guarantees and empirical validation