L\'evy walks and other theoretical models of optimal foraging have been successfully used to describe real-world scenarios, attracting attention in several fields such as economy, physics, ecology, and evolutionary biology. However, it remains unclear in most cases which strategies maximize foraging efficiency and whether such strategies can be learned by living organisms. To address these questions, we model foragers as reinforcement learning agents. We first prove theoretically that maximizing rewards in our reinforcement learning model is equivalent to optimizing foraging efficiency. We then show with numerical experiments that our agents learn foraging strategies which outperform the efficiency of known strategies such as L\'evy walks.
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