Interpretable Reinforcement Learning

Summary: Reinforcement learning methods that provide human-understandable decision processes, enabling transparency into how agents select actions. These approaches address the black-box nature of traditional RL by incorporating interpretable representations while maintaining performance.

Overview

Interpretable Reinforcement Learning focuses on developing RL algorithms where the decision-making process can be understood and explained to humans. Unlike traditional RL methods that learn complex, opaque policies, interpretable approaches aim to maintain transparency while preserving performance.

The field has evolved from manual approaches requiring domain expertise to automated methods that can systematically select interpretable representations. A key breakthrough is recognizing that interpretability in RL should focus on decision-relevance rather than just human-understandable features. This insight led to the development of automated concept selection algorithms that preserve the decision structure of the original problem.

Concept-Based Models represent the primary framework, where agents first map observations to human-interpretable concepts, then make decisions based on these concepts. This two-stage process allows humans to understand both what the agent perceives (concept layer) and how it reasons (decision layer).

Key Details

Decision-Relevant Selection (DRS) is the first automated algorithm for concept selection in interpretable RL. It works by:

• Viewing concept selection through State Abstraction theory
• Minimizing abstraction error to preserve decision-making performance
• Selecting concepts that distinguish states requiring different optimal actions

Performance guarantees: DRS provides theoretical bounds showing that concept-based policies using decision-relevant concepts achieve near-optimal performance compared to the original MDP.

Empirical validation across multiple domains:

• Test-time intervention effectiveness: 40-87% improvement when humans correct concept predictions
• Environments tested: CartPole, MiniGrid, Pong, Boxing, and real-world glucose management
• Concept recovery: Can automatically recover manually curated concept sets while matching or exceeding their performance

Computational complexity: The concept selection problem is proven NP-hard, but tractable approximation algorithms exist using Mixed Integer Linear Programming.

Key insight: Concepts are decision-relevant if removing them would cause agents to confuse states that require different optimal actions. This connects interpretability directly to decision quality rather than just human comprehensibility.

Relationships

• Concept-Based Models — primary architectural framework for interpretable RL implementations
• State Abstraction — theoretical foundation enabling automated concept selection through abstraction error minimization
• Decision-Relevant Concepts — core principle for determining which concepts preserve decision structure
• Test-Time Intervention — mechanism allowing human oversight and correction during deployment
• Reinforcement Learning — base paradigm extended with interpretability constraints
• Interpretable Machine Learning — broader field providing foundational principles for transparent AI
• Feature Selection — related optimization problem, but focused on prediction rather than decision-making
• Human-AI Interaction — application domain where interpretable RL enables effective human oversight
• Concept Bottleneck Models — related architecture from supervised learning adapted for RL contexts

Sources

• sources/selecting-decision-relevant-concepts-in-reinforcement-learning — introduced DRS algorithm, theoretical foundations, and empirical validation across multiple domains