Reinforcement Learning for GUI Agents: A Comprehensive Survey
A new arXiv paper (2604.27955) presents the first comprehensive overview of Reinforcement Learning (RL) applied to Graphical User Interface (GUI) agents. The authors propose a taxonomy organizing methods into Offline RL, Online RL, and Hybrid Strategies, with analyses of reward engineering, data efficiency, and technical innovations. Key trends include composite, multi-tier reward systems to balance reliability and scalability. The paper envisions GUI agents evolving toward 'digital inhabitants' capable of long-horizon tasks, safe exploration, and handling distribution shifts.
Key facts
- arXiv paper 2604.27955
- First comprehensive overview of RL and GUI agents
- Taxonomy: Offline RL, Online RL, Hybrid Strategies
- Analyses reward engineering and data efficiency
- Identifies tension between reliability and scalability
- Proposes composite, multi-tier reward systems
- Envisions digital inhabitants
- Published on arXiv
Entities
Institutions
- arXiv