Game AI Agent: G-Agent | 游戏智能体框架

G-Agent is a cross-terminal game automation testing framework based on Vision-Language Models (VLM) and AI Agent concepts.

G-Agent 是一个基于视觉大模型 (VLM) 和 AI 智能体理念的跨终端游戏自动化测试框架方案。

1. The Problem | 问题背景

Traditional game automation relies on Template Matching (finding a specific image on screen) or accessing internal game engine object trees. 传统游戏自动化依赖图像模板匹配或访问游戏引擎内部对象树。

• Issues:
• High Maintenance: If the UI icon changes slightly, the script fails.
  维护成本高：UI 图标微调，脚本即失效。
• Low Generalization: Scripts for one game level rarely work for another.
  泛化能力弱：关卡间的脚本难以复用。

2. The Solution: VLM + Agent | 解决方案

G-Agent aims to "See, Think, and Act like a Human Player". G-Agent 旨在实现“像人类玩家一样看懂画面、思考策略、操作游戏”。

2.1 See (看)

• Uses VLM (Vision-Language Models) (like GPT-4o, Gemini) to understand the game screen semantically.
• Instead of matching pixels, it understands "This is a 'Start Game' button" or "The character is low on health".
  利用 VLM 语义化理解游戏画面。它能理解“这是开始按钮”或“角色血量低”，而非仅仅匹配像素。

2.2 Think (想)

• The Agent analyzes the visual information and current game state to decide the next move.
• "Health is low -> I need to find a potion."
  智能体分析视觉信息和当前状态，决策下一步行动。“血量低 -> 我需要找药水。”

2.3 Act (做)

• Translates the decision into coordinate clicks or swipes on the screen.
  将决策转化为屏幕上的点击或滑动操作。

3. Value Proposition | 核心价值

• Robustness: Unaffected by minor UI changes or resolution differences.
  鲁棒性：不受 UI 微调或分辨率差异影响。
• Intelligence: Can handle complex logic that traditional scripts cannot (e.g., exploring a map).
  智能性：能处理传统脚本无法处理的复杂逻辑（如地图探索）。