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Source note3 min read

Explaining Reinforcement Learning Agents via Inductive Logic Programming

Source

arXiv

arxiv.org/abs/2607.13655v1

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Plain-Language Brief

This research aims to improve Reinforcement Learning (RL) policies by making them more transparent and interpretable. It proposes objective and planning-oriented metrics for policy explainability in RL settings using Inductive Logic Programming (ILP). The new metrics, including activation rate, feature coverage, syntactic distance, and semantic distance, quantify the alignment between symbolic rules and agent behavior, the role of features in decision-making, and the evolution of policies during training. Experiments across various RL domains demonstrate that these metrics provide fine-grained insights into domain features, learning dynamics, coordination patterns in Multi-Agent RL (MARL), and crucial insights for policy transfer and generalization.

What the Document Says

  • The research focuses on Explainable Reinforcement Learning (XRL) to make RL policies more transparent.
  • It introduces objective and planning-oriented metrics for policy explainability in RL settings using ILP.
  • The metrics include activation rate, feature coverage, syntactic distance, and semantic distance.
  • These metrics quantify alignment between symbolic rules and agent behavior, the role of features in decision-making, and the evolution of policies during training.
  • Experiments are conducted across different RL domains to demonstrate the effectiveness of the proposed metrics.

Key Concepts Mentioned

  • Reinforcement Learning
  • Explainable Reinforcement Learning
  • Inductive Logic Programming
  • policy explainability
  • metric
  • RL settings
  • feature coverage
  • syntactic distance
  • semantic distance
  • alignment between symbolic rules and agent behavior
  • evolution of policies
  • training
  • different RL domains

Claims Made by the Document

  • The proposed metrics provide fine-grained insights into domain features beyond classical approaches for global feature importance estimation.
  • They uncover coordination, specialization, and adaptation patterns in MARL.
  • They provide crucial insights for the transfer and generalization of action-specific policies.

Evidence Used

  • Experiments across different RL domains show that the proposed metrics highlight action-specific learning dynamics beyond global return, provide fine-grained insights into domain features, uncover coordination patterns in Multi-Agent RL (MARL), and provide crucial insights for the transfer and generalization of action-specific policies.

Methods / Approach

  • Inductive Logic Programming
  • defining a novel set of explainability metrics

Limitations Stated by the Authors

The systematic quantification of the explainability degree of logical representations remains an open problem.

Limitations Noted by the Agent

Agent interpretation — not from the source.

It is unknown if these methods are applicable to all types of RL agents or if they require specific conditions to be effective.

Open Questions

  • Are these methods applicable to all types of RL agents?
  • Under what specific conditions are these methods most effective?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The research proposes new metrics for policy explainability in RL settings, but it does not cover all possible limitations. For a comprehensive understanding, additional questions have been identified.

Raw Abstract / Excerpt

Explainable Reinforcement Learning (XRL) seeks to make Reinforcement Learning (RL) policies more transparent and interpretable, a key requirement in safety-critical and human-centric scenarios. However, it is mostly based on user studies, thus targeting the needs of a specific audience and lacking shared evaluation metrics. On the other hand, logic-based approaches within eXplainable Artificial Intelligence (XAI) provide compact, human-readable abstractions of decision-making. However, the systematic quantification of the explainability degree of logical representations remains an open problem. This work aims to advance the state of the art in XRL by introducing objective and planning-oriented metrics for policy explainability in RL settings. At the same time, it contributes to the field of logic for XAI by providing a principled way to quantify the explainability of logical rules, moving beyond common-sense assessments and simple propositional fragments. We employ Inductive Logic Programming (ILP) to extract symbolic representations of RL policies and define a novel set of explainability metrics, including activation rate, feature coverage, syntactic distance and semantic distance. These metrics quantify alignment between symbolic rules and agent behavior, the role of features in decision-making, and the evolution of policies during training and across agents in single and multi-agent RL. Experiments across different RL domains show that the proposed metrics highlight action-specific learning dynamics beyond global return, provide fine-grained insights into domain features beyond classical approaches for global feature importance estimation, and uncover coordination, specialization, and adaptation patterns in MARL. Moreover, they provide crucial insights for the transfer and generalization of action-specific policies.