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Player-1: Learning to Play from Synthetic Experience

Paper recorded by Signals 4 on 2026-10-14 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-14 on arXiv · recorded by Signals 4 on 2026-10-15

Category: cs.AI · 人工智能 · first seen 2026-10-15

Abstract

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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#307 most recent of 960 cs.AI papers we have recorded · ↑ newer: Continuum Attention · ↓ older: One Task, Many Interfaces: Simple Cross-Interface Consistency Training
Cite this page: Player-1: Learning to Play from Synthetic Experience: the #307 most recent of 960 cs.AI papers we have recorded (as of 2026-10-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/player-1-learning-to-play-from-synthetic-experience.html
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