A Framework for Understanding and Visualizing Strategies of RL Agents

Agent behavior can be explained using logical formulas: The framework successfully characterizes reinforcement learning agent strategies using temporal logic formulas, moving beyond black-box decision-making to provide interpretable rules that describe why agents take specific action sequences rather than just what actions they take.

Clustering reveals distinct behavioral patterns: By grouping agent traces based on frequent action patterns, the framework identified that agents employ multiple distinct strategies depending on task conditions—demonstrating that even a single trained agent doesn't follow one monolithic decision rule but adapts its approach across different scenarios.

Visualization tools are essential for understanding sequential decisions: The paper emphasizes that understanding agent behavior in sequential decision tasks requires more than numerical analysis; the visualization tool showing unit movements helped reveal how environmental conditions trigger different behavioral clusters, suggesting that explainability for AI systems requires multi-modal presentation.

Expert and learned policies can be compared systematically: By applying the framework to both handcrafted expert policies and trained RL agents, the research enables direct comparison of how human-designed strategies differ from machine-learned ones—a capability valuable for validating whether AI systems learn strategies aligned with human expertise.

High-level feature extraction bridges the gap between raw data and interpretability: The custom feature extractor that converted low-level game replays into meaningful high-level descriptions of environment state and agent behavior demonstrates that effective explainability often requires domain-specific preprocessing to translate complex data into human-understandable concepts.