The Animal-AI Environment: A virtual laboratory for comparative cognition and artificial intelligence research

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.

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Publication Details

Journal
Behavior Research Methods
Published
2025-02-28
DOI
https://doi.org/10.3758/s13428-025-02616-3
Citations
3
Primary Topic
Reinforcement Learning in Robotics
Type
article
Field-Weighted Citation Impact
7.44
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The Animal-AI Environment: A virtual laboratory for comparative cognition and artificial intelligence research

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Behavior Research Methods
Reinforcement Learning in Robotics
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article

The Animal-AI Environment: A virtual laboratory for comparative cognition and artificial intelligence research

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article en
3 citations

Abstract

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.

Behavior Research MethodsVol. 57(4)
University of Hull (GB), University of Exeter (GB), University of Cambridge (GB), Leverhulme Trust (GB), Imperial College London (GB), Universitat Politècnica de València (ES)
Openalex Percentile: Top 4%
Reinforcement Learning in Robotics
7.44
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