# The Animal-AI Environment: Training and Testing Animal-Like Artificial Cognition

arXiv: https://arxiv.org/abs/1909.07483 · HTML: https://mlsanity.com/paper/1909.07483/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2019.

## Built on by (most cited first, 16 of 16)

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- [Inferring Capabilities from Task Performance with Bayesian Triangulation](https://mlsanity.com/paper/2309.11975/) (2023)
- [Adaptive Intelligence: leveraging insights from adaptive behavior in animals to build flexible AI systems](https://mlsanity.com/paper/2411.15234/) (2024)
- [OPEn: An Open-ended Physics Environment for Learning Without a Task](https://mlsanity.com/paper/2110.06912/) (2021)
- [Extending Environments To Measure Self-Reflection In Reinforcement Learning](https://mlsanity.com/paper/2110.06890/) (2021)
- [Sequential memory improves sample and memory efficiency in Episodic Control](https://mlsanity.com/paper/2112.14734/) (2021)
- [The Animal-AI Environment: A Virtual Laboratory For Comparative Cognition and Artificial Intelligence Research](https://mlsanity.com/paper/2312.11414/) (2023)
- [A learning gap between neuroscience and reinforcement learning](https://mlsanity.com/paper/2104.10995/) (2021)
- [Discretization of continuous input spaces in the hippocampal autoencoder](https://mlsanity.com/paper/2405.14600/) (2024)
- [NiceWebRL: a Python library for human subject experiments with reinforcement learning environments](https://mlsanity.com/paper/2508.15693/) (2025)
- [AIGenC: An AI generalisation model via creativity](https://mlsanity.com/paper/2205.09738/) (2022)
- [Disentangled Representations for Causal Cognition](https://mlsanity.com/paper/2407.00744/) (2024)
- [From Pixels to Digital Agents: An Empirical Study on the Taxonomy and Technological Trends of Reinforcement Learning Environments](https://mlsanity.com/paper/2603.23964/) (2026)

## Abstract (arXiv)

Recent advances in artificial intelligence have been strongly driven by the use of game environments for training and evaluating agents. Games are often accessible and versatile, with well-defined state-transitions and goals allowing for intensive training and experimentation. However, agents trained in a particular environment are usually tested on the same or slightly varied distributions, and solutions do not necessarily imply any understanding. If we want AI systems that can model and understand their environment, we need environments that explicitly test for this. Inspired by the extensive literature on animal cognition, we present an environment that keeps all the positive elements of standard gaming environments, but is explicitly designed for the testing of animal-like artificial cognition.