The Animal-AI Environment: A Virtual Laboratory For Comparative Cognition and Artificial Intelligence Research
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.
Keywords
Cite
@article{arxiv.2312.11414,
title = {The Animal-AI Environment: A Virtual Laboratory For Comparative Cognition and Artificial Intelligence Research},
author = {Konstantinos Voudouris and Ibrahim Alhas and Wout Schellaert and Matteo G. Mecattaf and Ben Slater and Matthew Crosby and Joel Holmes and John Burden and Niharika Chaubey and Niall Donnelly and Matishalin Patel and Marta Halina and José Hernández-Orallo and Lucy G. Cheke},
journal= {arXiv preprint arXiv:2312.11414},
year = {2025}
}
Comments
37 pages, 16 figures, 6 tables