The problem the researchers set out to solve is one of infrastructure. Studying how people and autonomous agents make decisions together requires an unusual combination of capabilities: a simulation environment flexible enough to model social dilemmas or cooperative tasks, reinforcement learning machinery to train AI partners, networking code to synchronize multiple participants in real time, and the mundane apparatus of online experiments, including consent forms, waiting rooms, matchmaking, surveys, and data collection. In most labs, each of these pieces has been rebuilt from scratch for every study, a burden that pushes researchers toward simpler proxies such as surveys, rule-based expert systems, or Wizard-of-Oz setups in which a human secretly plays the role of the AI. The result is a literature in which state-of-the-art learning agents are rarely tested against real people.

CoGrid attacks the first half of the problem. It is a multi-agent, grid-based simulation library inspired by Minigrid, a widely used single-agent toolkit, but redesigned so that any number of agents—human or artificial—can inhabit the same grid. Environments are formalized as partially observable Markov decision processes, with modular components for observations, actions, and rewards that researchers can mix, extend, and customize through configuration files and small Python classes. Crucially, CoGrid adopts the PettingZoo API, the emerging standard for multi-agent reinforcement learning, so any environment built with it plugs directly into existing training libraries. Agents are implemented as first-class grid objects rather than hidden state tracked outside the world, which is precisely the design choice that made the original Minigrid single-agent only.

The most distinctive feature of CoGrid is its dual backend. All simulation code runs through a backend-agnostic array namespace that dispatches to either NumPy or JAX at runtime. With the JAX backend active, environment functions are just-in-time compiled and can be vectorized across thousands of parallel environment instances on a GPU, enabling the high-throughput training that modern reinforcement learning demands. With the NumPy backend, the identical environment code runs without any JAX dependency, which makes it compatible with Pyodide, a compilation of Python to WebAssembly that allows code to execute directly inside a web browser. This single-implementation approach means a researcher can train an agent at scale on hardware and then deploy the exact same environment to participants’ browsers, something JAX-only libraries such as JaxMARL and NAVIX cannot do.

The empirical payoff of that architecture is striking. In a demonstration recreating the Overcooked cooperative cooking environment, CoGrid’s JAX backend scaled from roughly 4,500 simulation steps per second with a single instance to 5.6 million steps per second with 1,024 parallel instances running on a single NVIDIA GeForce RTX 3090, a throughput improvement of about 1.9 times over JaxMARL’s implementation of the same task. The NumPy backend is far slower, at around 450 steps per second, but it is the only variant of the three compared that can run natively in a browser. The authors argue this trade-off is the point: rapid prototyping, accelerated training, and web deployment from one codebase.

MUG handles the second half of the pipeline: turning a simulation into an actual experiment. It takes any Python environment that follows the Gymnasium or PettingZoo APIs and makes it playable in the browser, without game engines or a video-game development cycle. Experiments are composed as sequences of scenes—landing pages, instructions, waiting rooms, gameplay, surveys—stitched together by a Stager that can randomize or counterbalance conditions at the participant level. Data collection is handled through callbacks that fire at key events, logging actions, states, and custom variables to disk or external databases, and the platform generates completion codes for recruitment platforms such as Prolific and Amazon Mechanical Turk. Participants can even be filtered in advance by browser, device, or network latency.

The genuinely novel engineering lies in how MUG deals with latency. Most existing platforms run the environment on a server and stream frames to participants, which means every action requires a network round trip. At the 30 to 60 frames per second typical of real-time tasks, even moderate latency renders such interactions unusable, and the problem compounds in multiplayer settings where all players’ inputs must reach the server before the world can advance. MUG instead offers client-side execution, running the Python environment inside the participant’s browser via Pyodide, and for multiplayer it implements GGPO-style rollback netcode, a technique borrowed from latency-sensitive fighting games. Each client runs a deterministic copy of the environment with a shared random seed, applies its own input immediately, predicts the remote players’ inputs, and, when a prediction proves wrong, rolls back to the last confirmed state and silently re-simulates the intervening frames with the correct actions.

To demonstrate the framework end to end, the team ran proof-of-concept studies with human participants recruited through Amazon Mechanical Turk, approved by Carnegie Mellon’s Institutional Review Board. In the Overcooked studies, 29 participants played 20 episodes alongside a fixed reinforcement learning partner trained with Proximal Policy Optimization, while 40 participants played in human–human pairs; after exclusions, 23 and 36 participants respectively remained. The results echoed earlier findings from the original Overcooked-AI work: AI–AI pairs delivered roughly 7.5 dishes per episode, far outperforming human–AI pairs, whose performance stayed flat over time even as human–human pairs steadily improved by developing shared strategies from scratch.

A subtler behavioral pattern emerged in the contribution data. Although total team performance in human–AI pairs barely changed across episodes, humans gradually shifted toward delivering finished dishes rather than placing onions in the pot, quietly ceding the cooking role to the AI even though this produced no measurable gain. That dissociation—stable performance but evolving division of labor—is exactly the kind of psychological phenomenon the authors say the platform is built to expose, touching on questions of trust, role adoption, and complementarity in mixed human–AI teams. A second case study using the fast-paced physics game Slime Volleyball, which is not grid-based, showed that MUG works with external Gymnasium environments too, with human–AI pairs sustaining longer rallies than human–human pairs thanks to the trained agent’s higher baseline skill.

The authors are candid about limitations. CoGrid is confined to grid-based worlds and cannot model continuous control or physics-heavy dynamics, and MUG currently supports no explicit communication—no text chat or structured messaging—so all interaction is mediated through actions and observations. Communication features are planned for future releases. Even so, the framework fills a conspicuous gap. Existing environment libraries either require non-Python languages, lack hardware acceleration, or cannot run in browsers, and existing experiment platforms do not jointly support standard APIs, real-time multi-human play, and client-side execution. By lowering the barrier to entry on both fronts, CoGrid and MUG are intended to let researchers ask novel questions about how humans and machines coordinate, compete, and adapt to one another—questions that will only grow more urgent as autonomous agents enter everyday social life. Both tools, with documentation and source code, are freely available to the research community.

Subject of Research: An open-source framework for multi-agent simulation and web-based human–AI interaction experiments

Article Title: CoGrid & the Multi-User Gymnasium: A framework for multi-agent experimentation

Article References: McDonald, C., & Gonzalez, C. (2026). CoGrid & the Multi-User Gymnasium: A framework for multi-agent experimentation. Behavior Research Methods, 58(11), Article 295. https://doi.org/10.3758/s13428-026-03078-x

Image Credits: AI Generated

DOI: 10.3758/s13428-026-03078-x

Keywords: human-AI interaction, multi-agent systems, reinforcement learning, CoGrid, Multi-User Gymnasium, open-source software, web-based experiments, PettingZoo, JAX, rollback netcode, Overcooked, behavioral research methods