INSIGHT: End-to-End Neuro-Symbolic Visual Reinforcement Learning with Language Explanations
Neuro-symbolic reinforcement learning (NS-RL) has emerged as a promising paradigm for explainable decision-making, characterized by the interpretability of symbolic policies. NS-RL entails structured state representations for tasks with visual observations, but previous methods are unable to refine the structured states with rewards due to a lack of efficiency. Accessibility also remains to be an issue, as extensive domain knowledge is required to interpret symbolic policies. In this paper, we present a framework for learning structured states and symbolic policies jointly, whose key idea is to distill vision foundation models into a scalable perception module and refine it during policy learning. Moreover, we design a pipeline to generate language explanations for policies and decisions using large language models. In experiments on nine Atari tasks, we verify the efficacy of our approach, and we also present explanations for policies and decisions.
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