TencentARC / Player-1

**Player-1** is an agentic post-training pipeline for learning general game control. Its workflow includes:
- **Supervised fine-tuning (SFT)** on **Player-1-Data**, curated gameplay demonstrations that initialize computer-use models for game control.
- **Reinforcement learning (RL)** in **Player-1-Gym**, a suite of sixteen synthetic environments, to build experience through interaction with verifiable rewards.
- **Evaluation on benchmarks** including **MineDojo-300**, **ViZDoom-10**, and **GameWorld** to assess transfer to games beyond the RL training suite.
https://github.com/user-attachments/assets/8b55b5f3-f93f-4d1d-a767-257de0f742c2
## 📢 News
- **[2026.10.14]** Released the paper and project page.
## Player-1 Overview
Player-1 training and evaluation pipeline: **Computer-use model → SFT on Player-1-Data → RL in Player-1-Gym → Evaluation in external games**
### 1/ Player-1-Data
We curate **6,044 gameplay trajectories from 172 games**, with recorded keyboard and mouse actions mapped to a shared game-control action space. Each training example includes task instructions, screenshot observations, reasoning, and actions for supervised learning.
### 2/ Player-1-Gym
**Sixteen synthetic 2D and 3D environments** cover aiming, combat, driving, navigation, and UI interaction. All use screenshot observations and keyboard and mouse controls, with verifiable rewards computed directly from game state. Randomizing **appearance, camera, scene, and control** broadens the agent's training experience.
### 3/ Evaluation Benchmarks
We evaluate transfer to games outside the RL training suite:
- **MineDojo-300 (300 tasks):** Goal-directed control in Minecraft's open-ended world.
- **ViZDoom-10 (10 maps):** First-person navigation, aiming, and combat in ViZDoom.
- **GameWorld (170 tasks):** Game control across 34 browser games with diverse mechanics.
## 🚀 Release Plan
> [!IMPORTANT]
> We are actively preparing the following releases. Thank you for starring 🌟 this repo and following our updates!
- [x] Paper & Project page
- [ ] Player-1-Data demonstration dataset
- [ ] Player-1-Gym environments
- [ ] Model checkpoints: Player-1-9B and Player-1-35B-A3B
- [ ] Training and evaluation code
## Citation
If you find our work useful, please consider citing us!
 [code]