Neuro-Symbolic Agent with ASP for Robust Exception Learning in Text-Based Games

Abstract

Text-based games (TBGs) present a significant challenge in natural language processing (NLP) by requiring reinforcement learning (RL) agents to combine language comprehension with reasoning. A primary difficulty for these agents is achieving generalization across multiple games, particularly in handling both familiar and novel objects. While deep RL approaches excel in scenarios with known objects, they struggle with unseen ones. Commonsense-augmented RL agents address this but often lack interpretability and transferability. To address these limitations, we propose a neuro-symbolic framework that integrates symbolic reasoning with neural RL models. Our approach utilizes inductive logic programming (ILP) to learn symbolic rules dynamically. We show that this hybrid agent outperforms pure neural agents in handling familiar objects. Additionally, we introduce a novel generalization approach based on information gain and WordNet that helps our agent to excel in the test sets with unseen objects as well.