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To make good decisions in the real world people need efficient planningstrategies because their computational resources are limited. Knowing whichplanning strategies would work best for people in different situations would bevery useful for understanding and improving human decision-making. But ourability to compute those strategies used to be limited to very small and verysimple planning tasks. To overcome this computational bottleneck, we introducea cognitively-inspired reinforcement learning method that can overcome thislimitation by exploiting the hierarchical structure of human behavior. Thebasic idea is to decompose sequential decision problems into two sub-problems:setting a goal and planning how to achieve it. This hierarchical decompositionenables us to discover optimal strategies for human planning in larger and morecomplex tasks than was previously possible. The discovered strategiesoutperform existing planning algorithms and achieve a super-human level ofcomputational efficiency. We demonstrate that teaching people to use thosestrategies significantly improves their performance in sequentialdecision-making tasks that require planning up to eight steps ahead. Bycontrast, none of the previous approaches was able to improve human performanceon these problems. These findings suggest that our cognitively-informedapproach makes it possible to leverage reinforcement learning to improve humandecision-making in complex sequential decision-problems. Future work canleverage our method to develop decision support systems that improve humandecision making in the real world.

Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning