Proceedings

Counterfactual Explanations for Continuous Action Reinforcement Learning

Shuyang Dong, Shangtong Zhang, Lu Feng

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 5039-5046.

https://doi.org/10.24963/ijcai.2025/561

Keywords

  • Machine Learning: ML: Reinforcement learning
  • Machine Learning: ML: Explainable/Interpretable machine learning
  • AI Ethics, Trust, Fairness: ETF: Explainability and interpretability

Abstract

Reinforcement Learning (RL) has shown great promise in domains like healthcare and robotics but often struggles with adoption due to its lack of interpretability. Counterfactual explanations, which address ``what if” scenarios, provide a promising avenue for understanding RL decisions but remain underexplored for continuous action spaces. We propose a novel approach for generating counterfactual explanations in continuous action RL by computing alternative action sequences that improve outcomes while minimizing deviations from the original sequence. Our approach leverages a distance metric for continuous actions and accounts for constraints such as adhering to predefined policies in specific states. Evaluations in two RL domains, Diabetes Control and Lunar Lander, demonstrate the effectiveness, efficiency, and generalization of our approach, enabling more interpretable and trustworthy RL applications.

Shuyang Dong, Shangtong Zhang, and Lu Feng. 2025. Counterfactual Explanations for Continuous Action Reinforcement Learning. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 5039–5046. https://doi.org/10.24963/ijcai.2025/561
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