Awesome Reinforcement Learning for GUI Agents

This repository provides a comprehensive and curated list of research papers, datasets, and tools focused on Reinforcement Learning (RL) in GUI Agents. GUI agents are intelligent systems that perceive graphical interfaces visually and execute tasks through human-like inputs (click, swipe, type).

📄 Based on the survey: GUI Agents with Reinforcement Learning: Toward Digital Inhabitants

---

📋 Table of Contents

• 🔔 News
• 🌟 Introduction
• 📚 Related Surveys
• 🤝 Contributing
• 🏗️ RL Methods
  • Offline Reinforcement Learning
  • Online Reinforcement Learning
  • Hybrid Strategies
• 🎨 Key Dimensions
  • Reward Engineering
  • Data Efficiency
  • Technical Innovations
• 📊 Training Resources
  • Datasets
  • Interactive Environments
  • RL Infrastructure
• 📝 Citation

Overview of the survey structure. We organize our analysis into three main pillars: RL Methods, Key Dimensions, and Training Resources.

---

🔔 News

• [2026-04-30] 📄 Our survey "GUI Agents with Reinforcement Learning: Toward Digital Inhabitants" is now available on arXiv!
• [2026-04-19] 🚀 Repository created! Stay tuned for more updates on RL-based GUI Agents.

---

🌟 Introduction

Reinforcement Learning for GUI agents addresses the core difficulties of GUI automation: long-horizon credit assignment under sparse rewards, distribution shift across evolving interfaces, and safe exploration. We organize the landscape into three methodological paradigms:

• Offline RL: Learning from static datasets without environment interaction.
• Online RL: Refinement through continuous trial and error in dynamic environments.
• Hybrid Strategies: Bridging pre-training and adaptation via semi-online methods and world models.

Overview of the RL training pipeline for GUI agents. The agent perceives the GUI environment through screenshots, reasons about the task, and executes actions. RL optimizes the policy through reward signals derived from task completion, visual grounding accuracy, and intermediate reasoning quality.

Timeline of GUI Agent Development from rule-based systems to the multimodal LLM era.

---

📚 Related Surveys

---

🤝 Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

---

🏗️ RL Methods

Overview of the RL training pipeline for GUI agents. The agent perceives the GUI environment through screenshots, reasons about the task, and executes actions. RL optimizes the policy through reward signals derived from task completion, visual grounding accuracy, and intermediate reasoning quality.

Frontier Models

Reinforcement Learning Paradigms

Offline RFT Methods

Representative Methods

Emerging Directions

Algorithmic Advances: Exploration and Multi-Turn Optimization

🎨 Key Dimensions

Reward Engineering

The process of defining objective feedback signals for GUI tasks.

The Reward Engineering Pyramid balances accuracy and generality for GUI Agents: rule-based rewards offer precision, while learned rewards and LLM-as-judge enable semantic depth.

Reward Engineering

Rule-Based Rewards

LLM-as-Judge Rewards

Learned Rewards

Data Efficiency

Synthetic Data via World Models

Enhancement of Human Demonstrations

Iterative Self-Improvement

Technical Innovations

Multimodal Perception: Active and Adaptive Visual Grounding

Memory and Planning: Sustaining Context over Long Horizons

📊 Training Resources

Datasets

This pyramid depicts a four-stage data-training pipeline for agent capability, progressing from static data imitation to offline RL, synthetic simulation, and online RL.

Demonstration and Trajectory Datasets

Perception and Grounding Datasets

Synthetic and RL-Generated Corpora

Interactive Environments

Web and Browser Environments

Desktop and OS Environments

Mobile Environments

Cross-Platform Trends and Synthesis

RL Infrastructure

An asynchronous distributed architecture for GUI RL agent training, decoupling slow environment interaction from fast GPU learning.

VLM-RL Algorithm Libraries and Framework Evolution

Distributed Rollout and Training Architectures

Reward Engineering and Verification Systems

Memory Management and Long-Horizon Reasoning

Integration and Ecosystem Standardization

📝 Citation

If you find this repository or our survey useful, please consider citing:

@article{hu2026gui,
  title={GUI Agents with Reinforcement Learning: Toward Digital Inhabitants},
  author={Hu, Junan and Liu, Jian and Lai, Jingxiang and Hu, Jiarui and Sheng, Yiwei and Chen, Shuang and Li, Jian and Du, Dazhao and Guo, Song},
  journal={arXiv preprint arXiv:2604.27955},
  year={2026}
}