RL for GUI Agents

These agents translate natural-language goals into grounded clicks, typing, navigation, and tool use while adapting to changing screens and long workflows. Most systems begin with supervised demonstrations, then optimize policies using PPO- or GRPO-style online learning, offline trajectory data, or hybrid training; rewards range from task-completion checks to step-level critics, environment-state verification, and spatial feedback. To reduce costly interaction, researchers use synthetic or mocked apps, trajectory reuse, world models, scalable sandboxes, and curricula, with evaluation on suites such as AndroidWorld, OSWorld, and ScreenSpot. Key challenges include trustworthy rewards under partial observability, efficient exploration and credit assignment for sparse long-horizon tasks, and reliable transfer to unseen apps, users, and real devices.