---
license: apache-2.0
pretty_name: OmniGameArena
task_categories:
- reinforcement-learning
tags:
- games
- benchmark
- agents
- game-playing
- unreal-engine
viewer: false
---

# OmniGameArena

[**Project Page**](https://mxlin043.github.io/OmniGameArena/) | [**Paper**](https://huggingface.co/papers/2606.09826) | [**GitHub**](https://github.com/mxlin043/OmniGameArena)

**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](https://github.com/mxlin043/OmniGameArena).

### 1. Install the agent code
```bash
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:
```bash
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

```bibtex
@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**.