license: apache-2.0
pretty_name: OmniGameArena
task_categories:
- reinforcement-learning
tags:
- games
- benchmark
- agents
- game-playing
- unreal-engine
viewer: false
OmniGameArena
Project Page | Paper | GitHub
OmniGameArena is a unified benchmark for evaluating game-playing vision-language model (VLM) agents across a diverse suite of 12 interactive video games built with Unreal Engine 5.
This repository provides the packaged, ready-to-run game environments used by the benchmark.
Contents
| File | Platform | Size |
|---|---|---|
OmniGameArena_Windows.zip |
Windows (x64) | ~2.8 GB |
OmniGameArena_Linux.zip |
Linux | ~2.9 GB |
Each archive is a single self-contained build of the OmniGameArena environment for the corresponding platform.
Games and Modes
OmniGameArena contains 12 games, spanning three interaction modes:
- Solo (7 games) — ObstacleRun2D, ObstacleRun3D, LastStand, MonsterShoot, SceneEscape, CueChase, SoloCraft. Measures an agent's individual ability.
- PvP (Player vs. Player) (3 games) — SkyDuel, CrystalGuard, MidlineClash. Two agents compete head-to-head.
- Coop (Cooperative) (2 games) — SharedFloor, HandoffRun. Multiple agents coordinate to succeed together.
Sample Usage
To use these environments with the benchmark agents, first download and launch the UE5 environment build, then use the agent code from the GitHub repository.
1. Install the agent code
conda create -n omnigamearena python=3.10
conda activate omnigamearena
pip install -r requirements.txt
2. Launch the Environment
Launch the downloaded executable for your platform. The environment waits for the agent over TCP (default 127.0.0.1:12345).
3. Run a benchmark
Point the runner at the running environment's host and port:
python scripts/run_benchmark.py \
--config configs/vlm/cold_start/solo/obstacle_run_2d/vanilla_pdq.yaml \
--host 127.0.0.1 --port 12345
Controls
The environment can also be played manually:
| Key | Action |
|---|---|
P |
Open the map selector |
R |
Reset the current game |
Citation
@article{lin2026omnigamearena,
title = {OmniGameArena: A Unified UE5 Benchmark for VLM Game Agents with Improvement Dynamics},
author = {Lin, Mingxian and Qian, Shengju and Liu, Yuqi and Huang, Yi-Hua and Wang, Yiyu and Huang, Wei and Li, Yitang and Zhang, Fan and Hu, Zeyu and Zhu, Lingting and Wang, Xin and Qi, Xiaojuan},
journal = {arXiv preprint arXiv:2606.09826},
year = {2026}
}
License
This dataset is released under the Apache License 2.0.