Neuro-Symbolic Reinforcement Learning with First-Order Logic

@inproceedings{Kimura2021NeuroSymbolicRL,
  title={Neuro-Symbolic Reinforcement Learning with First-Order Logic},
  author={Daiki Kimura and Masaki Ono and Subhajit Chaudhury and Ryosuke Kohita and Akifumi Wachi and Don Joven Agravante and Michiaki Tatsubori and Asim Munawar and Alexander G. Gray},
  booktitle={EMNLP},
  year={2021}
}
Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided. In order to achieve fast convergence and interpretability for the policy in RL, we propose a novel RL method for text-based games with a recent neurosymbolic framework called Logical Neural Network, which can learn symbolic and interpretable rules in their differentiable network. The method is first to extract first-order logical facts from text… 
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