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Knowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes

arXiv cs.AI · 2026-07-15 Cached

This paper presents KGRL, a neuro-symbolic algorithm that uses domain knowledge expressed in Datalog to prune actions and constrain parameters in Parametrized Action Markov Decision Processes, improving sample efficiency and episodic return over state-of-the-art baselines.

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