Qing Li
Qing Li
Description: End-to-End Neuro-Symbolic Reinforcement Learning with Textual 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 cannot refine the structured states with rewards due to a lack of efficiency. Accessibility also remains an issue, as extensive domain knowledge is required to interpret symbolic policies. In this paper, we present a neuro-symbolic f
INSIGHT consists of three components: a perception module, a policy learning module, and a policy explanation module. The perception module learns to predict object coordinates using a frame-symbol dataset distilled from vision foundation models. The policy learning module is responsible for learning coordinate-based symbolic policies. In particular, to address with the limited expressiveness of object coordinates, it uses a neural actor to interact with the environment. The policy explanation module can ge
Here are the segmentation results for nine Atari games, before and after policy learning. It has been observed that the accuracy of policy-irrelevant objects decreases, whereas the accuracy of policy-related objects increases.
Qing Li
Qing Li