# The Animal-AI Environment: A Virtual Laboratory For Comparative Cognition and Artificial Intelligence Research

arXiv: https://arxiv.org/abs/2312.11414 · HTML: https://mlsanity.com/paper/2312.11414/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 91 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 21 of 21)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge](https://mlsanity.com/paper/2206.08853/) (2022)
- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks](https://mlsanity.com/paper/2306.13831/) (2023)
- [ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation](https://mlsanity.com/paper/2007.04954/) (2020)
- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [Human-Timescale Adaptation in an Open-Ended Task Space](https://mlsanity.com/paper/2301.07608/) (2023)
- [Dopamine: A Research Framework for Deep Reinforcement Learning](https://mlsanity.com/paper/1812.06110/) (2018)
- [The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors](https://mlsanity.com/paper/2101.11071/) (2021)
- [Investigating Human Priors for Playing Video Games](https://mlsanity.com/paper/1802.10217/) (2018)
- [Avalon: A Benchmark for RL Generalization Using Procedurally Generated Worlds](https://mlsanity.com/paper/2210.13417/) (2022)
- [The Animal-AI Environment: Training and Testing Animal-Like Artificial Cognition](https://mlsanity.com/paper/1909.07483/) (2019)
- [Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents](https://mlsanity.com/paper/1801.08116/) (2018)
- [Using Unity to Help Solve Intelligence](https://mlsanity.com/paper/2011.09294/) (2020)
- [Inferring Capabilities from Task Performance with Bayesian Triangulation](https://mlsanity.com/paper/2309.11975/) (2023)
- [OPEn: An Open-ended Physics Environment for Learning Without a Task](https://mlsanity.com/paper/2110.06912/) (2021)
- [crea.blender: A Neural Network-Based Image Generation Game to Assess Creativity](https://mlsanity.com/paper/2008.05914/) (2020)
- [Sequential memory improves sample and memory efficiency in Episodic Control](https://mlsanity.com/paper/2112.14734/) (2021)
- [Fixed β-VAE Encoding for Curious Exploration in Complex 3D Environments](https://mlsanity.com/paper/2105.08568/) (2021)

## Built on by (most cited first, 2 of 2)

- [Children's Intelligence Tests Pose Challenges for MLLMs? KidGym: A 2D Grid-Based Reasoning Benchmark for MLLMs](https://mlsanity.com/paper/2603.20209/) (2026)
- [Using profiles of cognitive capability to assess AI suitability for workplace tasks](https://mlsanity.com/paper/2608.25623/) (2026)

## Abstract (arXiv)

The Animal-AI Environment is a unique game-based research platform designed to facilitate collaboration between the artificial intelligence and comparative cognition research communities. In this paper, we present the latest version of the Animal-AI Environment, outlining several major features that make the game more engaging for humans and more complex for AI systems. These features include interactive buttons, reward dispensers, and player notifications, as well as an overhaul of the environment's graphics and processing for significant improvements in agent training time and quality of the human player experience. We provide detailed guidance on how to build computational and behavioural experiments with the Animal-AI Environment. We present results from a series of agents, including the state-of-the-art deep reinforcement learning agent Dreamer-v3, on newly designed tests and the Animal-AI Testbed of 900 tasks inspired by research in the field of comparative cognition. The Animal-AI Environment offers a new approach for modelling cognition in humans and non-human animals, and for building biologically inspired artificial intelligence.