Humans use prior knowledge to efficiently solve novel tasks, but how they structure past knowledge to enable such fast generalization is not well understood. The options framework in hierarchical reinforcement learning provides a candidate framework for representing temporally extended and transferable strategies. We developed a novel sequential decision making protocol to test if humans learn and transfer multi-step options. In a series of experiments, we found transfer effects at multiple levels of abstraction that could not be explained by flat reinforcement learning models or hierarchical models lacking temporal abstraction. We extended the options framework to develop a quantitative model that blends temporal and state abstractions. Our model captures the transfer effects observed in human participants. Our results provide evidence that humans create and compose options, and use them to explore in novel contexts, consequently transferring past knowledge and speeding up learning.
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