INSIGHT: End-to-End Neuro-Symbolic Visual Reinforcement Learning with Language Explanations
CoRR(2024)
摘要
Neuro-symbolic reinforcement learning (NS-RL) has emerged as a promising
paradigm for explainable decision-making, characterized by the interpretability
of symbolic policies. For tasks with visual observations, NS-RL entails
structured representations for states, but previous algorithms are unable to
refine the structured states with reward signals due to a lack of efficiency.
Accessibility is also an issue, as extensive domain knowledge is required to
interpret current symbolic policies. In this paper, we present a framework that
is capable of learning structured states and symbolic policies simultaneously,
whose key idea is to overcome the efficiency bottleneck by distilling vision
foundation models into a scalable perception module. Moreover, we design a
pipeline that uses large language models to generate concise and readable
language explanations for policies and decisions. In experiments on nine Atari
tasks, our approach demonstrates substantial performance gains over existing
NSRL methods. We also showcase explanations for policies and decisions.
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