Proceedings

Deduction with Induction: Combining Knowledge Discovery and Reasoning for Interpretable Deep Reinforcement Learning

Haodi Zhang, Xiangyu Zeng, Junyang Chen, Yuanfeng Song, Rui Mao, Fangzhen Lin

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 6984-6992.

https://doi.org/10.24963/ijcai.2025/777

Keywords

  • Machine Learning: ML: Reinforcement learning
  • Machine Learning: ML: Knowledge-aided learning
  • Machine Learning: ML: Neuro-symbolic methods/Abductive Learning

Abstract

Deep reinforcement learning (DRL) has achieved remarkable success in dynamic decision-making tasks. However, its inherent opacity and cold start problem hinder transparency and training efficiency. To address these challenges, we propose HRL-ID, a neural-symbolic framework that combines automated rule discovery with logical reasoning within a hierarchical DRL structure. HRL-ID dynamically extracts first-order logic rules from environmental interactions, iteratively refines them through success-based updates, and leverages these rules to guide action execution during training. Extensive experiments on Atari benchmarks demonstrate that HRL-ID outperforms state-of-the-art methods in training efficiency and interpretability, achieving higher reward rates and successful knowledge transfer between domains.

Haodi Zhang, Xiangyu Zeng, Junyang Chen, Yuanfeng Song, Rui Mao, and Fangzhen Lin. 2025. Deduction with Induction: Combining Knowledge Discovery and Reasoning for Interpretable Deep Reinforcement Learning. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 6984–6992. https://doi.org/10.24963/ijcai.2025/777
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