# Adaptive patch foraging in deep reinforcement learning agents

arXiv: https://arxiv.org/abs/2210.08085 · HTML: https://mlsanity.com/paper/2210.08085/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 61 references that are in the map.
- First posted on arXiv 14 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning to reinforcement learn](https://mlsanity.com/paper/1611.05763/) (2016)
- [Maximum a Posteriori Policy Optimisation](https://mlsanity.com/paper/1806.06920/) (2018)
- [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019)
- [Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future](https://mlsanity.com/paper/2001.07092/) (2020)
- [On-Policy Deep Reinforcement Learning for the Average-Reward Criterion](https://mlsanity.com/paper/2106.07329/) (2021)
- [Jelly Bean World: A Testbed for Never-Ending Learning](https://mlsanity.com/paper/2002.06306/) (2020)
- [Learning Robust Real-Time Cultural Transmission without Human Data](https://mlsanity.com/paper/2203.00715/) (2022)
- [Foraging as an evidence accumulation process](https://mlsanity.com/paper/1809.05023/) (2018)
- [Stochastic dynamics of social patch foraging decisions](https://mlsanity.com/paper/2202.05761/) (2022)

## Built on by (most cited first, 3 of 3)

- [Reinforcement Learning for Active Matter](https://mlsanity.com/paper/2503.23308/) (2025)
- [A State Representation for Diminishing Rewards](https://mlsanity.com/paper/2309.03710/) (2023)
- [Primate-like perceptual decision making emerges through deep recurrent reinforcement learning](https://mlsanity.com/paper/2601.12577/) (2026)

## Abstract (arXiv)

Patch foraging is one of the most heavily studied behavioral optimization challenges in biology. However, despite its importance to biological intelligence, this behavioral optimization problem is understudied in artificial intelligence research. Patch foraging is especially amenable to study given that it has a known optimal solution, which may be difficult to discover given current techniques in deep reinforcement learning. Here, we investigate deep reinforcement learning agents in an ecological patch foraging task. For the first time, we show that machine learning agents can learn to patch forage adaptively in patterns similar to biological foragers, and approach optimal patch foraging behavior when accounting for temporal discounting. Finally, we show emergent internal dynamics in these agents that resemble single-cell recordings from foraging non-human primates, which complements experimental and theoretical work on the neural mechanisms of biological foraging. This work suggests that agents interacting in complex environments with ecologically valid pressures arrive at common solutions, suggesting the emergence of foundational computations behind adaptive, intelligent behavior in both biological and artificial agents.