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Inspired by human conscious planning, we propose Skipper, a model-basedreinforcement learning framework utilizing spatio-temporal abstractions togeneralize better in novel situations. It automatically decomposes the giventask into smaller, more manageable subtasks, and thus enables sparsedecision-making and focused computation on the relevant parts of theenvironment. The decomposition relies on the extraction of an abstracted proxyproblem represented as a directed graph, in which vertices and edges arelearned end-to-end from hindsight. Our theoretical analyses provide performanceguarantees under appropriate assumptions and establish where our approach isexpected to be helpful. Generalization-focused experiments validate Skipper'ssignificant advantage in zero-shot generalization, compared to some existingstate-of-the-art hierarchical planning methods.

Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning