Exploring Computational User Models for Agent Policy Summarization

Understand

AI agents are being developed to support high stakes decision-making processes from driving cars to prescribing drugs, making it increasingly important for human users to understand their behavior.

• Policy summarization methods aim to convey strengths and weaknesses of such agents by demonstrating their behavior in a subset of informative states.
• Some policy summarization methods extract a summary that optimizes the ability to reconstruct the agent's policy under the assumption that users will deploy inverse reinforcement learning.
• In this paper, we explore the use of different models for extracting summaries.