@inproceedings{chaudhury-etal-2023-learning,
    title = "Learning Symbolic Rules over {A}bstract {M}eaning {R}epresentations for Textual Reinforcement Learning",
    author = "Chaudhury, Subhajit  and
      Swaminathan, Sarathkrishna  and
      Kimura, Daiki  and
      Sen, Prithviraj  and
      Murugesan, Keerthiram  and
      Uceda-Sosa, Rosario  and
      Tatsubori, Michiaki  and
      Fokoue, Achille  and
      Kapanipathi, Pavan  and
      Munawar, Asim  and
      Gray, Alexander",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.373",
    doi = "10.18653/v1/2023.acl-long.373",
    pages = "6764--6776",
    abstract = "Text-based reinforcement learning agents have predominantly been neural network-based models with embeddings-based representation, learning uninterpretable policies that often do not generalize well to unseen games. On the other hand, neuro-symbolic methods, specifically those that leverage an intermediate formal representation, are gaining significant attention in language understanding tasks. This is because of their advantages ranging from inherent interpretability, the lesser requirement of training data, and being generalizable in scenarios with unseen data. Therefore, in this paper, we propose a modular, NEuro-Symbolic Textual Agent (NESTA) that combines a generic semantic parser with a rule induction system to learn abstract interpretable rules as policies. Our experiments on established text-based game benchmarks show that the proposed NESTA method outperforms deep reinforcement learning-based techniques by achieving better generalization to unseen test games and learning from fewer training interactions.",
}

Text-based reinforcement learning agents have predominantly been neural network-based models with embeddings-based representation, learning uninterpretable policies that often do not generalize well to unseen games. On the other hand, neuro-symbolic methods, specifically those that leverage an intermediate formal representation, are gaining significant attention in language understanding tasks. This is because of their advantages ranging from inherent interpretability, the lesser requirement of training data, and being generalizable in scenarios with unseen data. Therefore, in this paper, we propose a modular, NEuro-Symbolic Textual Agent (NESTA) that combines a generic semantic parser with a rule induction system to learn abstract interpretable rules as policies. Our experiments on established text-based game benchmarks show that the proposed NESTA method outperforms deep reinforcement learning-based techniques by achieving better generalization to unseen test games and learning from fewer training interactions.
Chaudhury, Subhajit Swaminathan, Sarathkrishna Kimura, Daiki Sen, Prithviraj Murugesan, Keerthiram Uceda-Sosa, Rosario Tatsubori, Michiaki Fokoue, Achille Kapanipathi, Pavan Munawar, Asim Gray, Alex er
Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning