Improve dataset card: add paper link, task category, and sample usage

README.md

@@ -1,21 +1,24 @@
 ---
 license: apache-2.0
 pretty_name: OmniGameArena
+task_categories:
+- reinforcement-learning
 tags:
-  - games
-  - benchmark
-  - agents
-  - game-playing
-  - unreal-engine
+- games
+- benchmark
+- agents
+- game-playing
+- unreal-engine
 viewer: false
 ---
 
 # OmniGameArena
 
-**OmniGameArena** is a benchmark for evaluating game-playing agents across a
-diverse suite of interactive video games. This dataset provides the
-**packaged, ready-to-run game environments** used by the benchmark, built with
-**Unreal Engine 5**.
+[**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
 
@@ -24,32 +27,58 @@ diverse suite of interactive video games. This dataset provides the
 | `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.
+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** — A single agent plays on its own and is evaluated against the game's
-  own score. Measures an agent's individual game-playing ability.
-- **PvP (Player vs. Player)** — Two agents compete head-to-head in the
-  same game. Performance is judged relatively, where one agent's gain is
-  another agent's loss. Measures competitive ability.
-- **Coop (Cooperative)** — Multiple agents share a common goal and must
-  coordinate with one another to succeed together. Measures cooperative and
-  communicative ability.
+- **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 be played with **keyboard and mouse**, or with a
-**connected gamepad**.
+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**.
+This dataset is released under the **Apache License 2.0**.