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Player-1: Learning to Play from Synthetic Experience
Ouyang 2026-10-14
Mingyu OuyangYiran WangSiyuan Hu
Computer-use agents have demonstrated strong capabilities in interpreting screens and executing keyboard and mouse actions. However, games, with their visually rich worlds and diverse mechanics, remain challenging and offer a demanding next step toward general digital control. Current game-agent learning pipelines often rely on large-scale demonstrations with limited coverage of dynamic game state
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Key Contributions
- Computer-use agents have demonstrated strong capabilities in interpreting screens and executing keyboard and mouse actions.
- However, games, with their visually rich worlds and diverse mechanics, remain challenging and offer a demanding next step toward general digital control.
- Current game-agent learning pipelines often rely on large-scale demonstrations with limited coverage of dynamic game state
Research Themes
AIResearch