# Optimal foraging strategies can be learned

arXiv: https://arxiv.org/abs/2303.06050 · HTML: https://mlsanity.com/paper/2303.06050/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 57 references that are in the map.
- First posted on arXiv 10 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [Emergence of Maps in the Memories of Blind Navigation Agents](https://mlsanity.com/paper/2301.13261/) (2023)
- [Projective simulation for classical learning agents: a comprehensive investigation](https://mlsanity.com/paper/1305.1578/) (2013)
- [On the role of theory and modeling in neuroscience](https://mlsanity.com/paper/2003.13825/) (2020)
- [Projective simulation with generalization](https://mlsanity.com/paper/1504.02247/) (2015)
- [Development of swarm behavior in artificial learning agents that adapt to different foraging environments](https://mlsanity.com/paper/2004.00552/) (2020)
- [Meta-learning within Projective Simulation](https://mlsanity.com/paper/1602.08017/) (2016)
- [On the convergence of projective-simulation-based reinforcement learning in Markov decision processes](https://mlsanity.com/paper/1910.11914/) (2019)
- [Collective defense of honeybee colonies: experimental results and theoretical modeling](https://mlsanity.com/paper/2010.07326/) (2020)

## Built on by (most cited first, 5 of 5)

- [Reinforcement Learning for Active Matter](https://mlsanity.com/paper/2503.23308/) (2025)
- [Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive Bias](https://mlsanity.com/paper/2402.10192/) (2024)
- [Learning to reset in target search problems](https://mlsanity.com/paper/2503.11330/) (2025)
- [Beyond Task Performance: A Metric-Based Analysis of Sequential Cooperation in Heterogeneous Multi-Agent Destructive Foraging](https://mlsanity.com/paper/2602.10685/) (2026)
- [Emergent aggregation from collective foraging](https://mlsanity.com/paper/2608.28046/) (2026)

## Abstract (arXiv)

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.