Derives an SDE describing the infinitesimal change in state distribution at each gradient step for neural actor-critic RL in continuous environments under vanishing learning rate in the infinite width limit.
From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning
2 Pith papers cite this work. Polarity classification is still indexing.
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STRIPS-WM induces an abstract transition graph from images, learns latent binary predicates and one grounded operator per action, then distills the predicates into a visual encoder for classical planning from novel start and goal images.
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From Ticks to Flows: Dynamics of Neural Reinforcement Learning in Continuous Environments
Derives an SDE describing the infinitesimal change in state distribution at each gradient step for neural actor-critic RL in continuous environments under vanishing learning rate in the infinite width limit.
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STRIPS-WM: Learning Grounded Propositional STRIPS-style World Models from Images
STRIPS-WM induces an abstract transition graph from images, learns latent binary predicates and one grounded operator per action, then distills the predicates into a visual encoder for classical planning from novel start and goal images.