Learning Symbolic Rules over Abstract Meaning Representations for
  Textual Reinforcement Learning
Subhajit Chaudhury Sarathkrishna Swaminathan Daiki Kimura Prithviraj Sen Keerthiram Murugesan Rosario Uceda-Sosa Michiaki Tatsubori Achille Fokoue Pavan Kapanipathi Asim Munawar Alexander Gray
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.