  The foraging behavior of animals is a paradigm of target search in nature.
Understanding which foraging strategies are optimal and how animals learn them
are central challenges in modeling animal foraging. While the question of
optimality has wide-ranging implications across fields such as economy,
physics, and ecology, the question of learnability is a topic of ongoing debate
in evolutionary biology. Recognizing the interconnected nature of these
challenges, this work addresses them simultaneously by exploring optimal
foraging strategies through a reinforcement learning framework. To this end, we
model foragers as 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, in the
paradigmatic model of non-destructive search, our agents learn foraging
strategies which outperform the efficiency of some of the best known strategies
such as L\'evy walks. These findings highlight the potential of reinforcement
learning as a versatile framework not only for optimizing search strategies but
also to model the learning process, thus shedding light on the role of learning
in natural optimization processes.
