@inproceedings{brasoveanu-dotlacil-2020-production,
    title = "Production-based Cognitive Models as a Test Suite for Reinforcement Learning Algorithms",
    author = "Brasoveanu, Adrian  and
      Dotlacil, Jakub",
    editor = "Chersoni, Emmanuele  and
      Jacobs, Cassandra  and
      Oseki, Yohei  and
      Pr{\'e}vot, Laurent  and
      Santus, Enrico",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.cmcl-1.3/",
    doi = "10.18653/v1/2020.cmcl-1.3",
    pages = "28--37",
    abstract = "We introduce a framework in which production-rule based computational cognitive modeling and Reinforcement Learning can systematically interact and inform each other. We focus on linguistic applications because the sophisticated rule-based cognitive models needed to capture linguistic behavioral data promise to provide a stringent test suite for RL algorithms, connecting RL algorithms to both accuracy and reaction-time experimental data. Thus, we open a path towards assembling an experimentally rigorous and cognitively realistic benchmark for RL algorithms. We extend our previous work on lexical decision tasks and tabular RL algorithms (Brasoveanu and Dotla{\v{c}}il, 2020b) with a discussion of neural-network based approaches, and a discussion of how parsing can be formalized as an RL problem."
}