 This work uses reinforcement learning to explore optimal foraging strategies and their learnability in a paradigmatic model of non-destructive search, showing that RL agents outperform known strategies and highlighting its potential as a versatile framework for modeling natural optimization processes. This article was authored by Gorka Munozgil, Andrea Lopez in CERA, Lucas J. Fidia, and others.