NSER uses zero-shot LLMs to induce behavioral rules from RL trajectories, grounds them in differentiable first-order logic, and applies the symbolic structures to dynamically reweight experience replay for better sample efficiency.
34th International Conference on Machine Learning: ICML 2017, Sydney, Australia, 6-11 August 2017, volume 1 of 8 , year=
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From Passive Reuse to Active Reasoning: Grounding Large Language Models for Neuro-Symbolic Experience Replay
NSER uses zero-shot LLMs to induce behavioral rules from RL trajectories, grounds them in differentiable first-order logic, and applies the symbolic structures to dynamically reweight experience replay for better sample efficiency.