CoGrid & the Multi-User Gymnasium: A Framework for Multi-Agent Experimentation

• URL: http://arxiv.org/abs/2604.15044v1
• Date: Thu, 16 Apr 2026 14:10:38 GMT
• Title: CoGrid & the Multi-User Gymnasium: A Framework for Multi-Agent Experimentation
• Abstract summary: CoGrid is a multi-agent grid-based simulation library with dual NumPy and JAX backends.<n>Multi-User Gymnasium translates such simulation environments directly into interactive web-based experiments.<n> MUG supports interactions with arbitrary numbers of humans and AI, utilizing either server-authoritative or peer-to-peer networking with rollback netcode to account for latency.
• Score: 0.17188280334580197
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing integration of artificial intelligence (AI) in everyday life brings with it new challenges and questions for regarding how humans interact with autonomous agents. Multi-agent experiments, where humans and AI act together, can offer important opportunities to study social decision making, but there is a lack of accessible tooling available to researchers to run such experiments. We introduce two tools designed to reduce these barriers. The first, CoGrid, is a multi-agent grid-based simulation library with dual NumPy and JAX backends. The second, Multi-User Gymnasium (MUG), translates such simulation environments directly into interactive web-based experiments. MUG supports interactions with arbitrary numbers of humans and AI, utilizing either server-authoritative or peer-to-peer networking with rollback netcode to account for latency. Together, these tools can enable researchers to deploy studies of human-AI interaction, facilitating inquiry into core questions of psychology, cognition, and decision making and their relationship to human-AI interaction. Both tools are open source and available to the broader research community. Documentation and source code is available at {cogrid, multi-user-gymnasium}.readthedocs.io. This paper details the functionality of these tools and presents several case studies to illustrate their utility in human-AI multi-agent experimentation.

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