Build a browser game and train an agent to play it
I used Grok to build the browser game Roofline. Task: The author used Grok to develop a browser game named Roofline and subsequently trained a PPO agent to play the live page. The workflow involved generating the game code and then creating an agent script that interacts with the game interface. The source does not specify the exact human role in the training loop or debugging process.; tools: Grok Bot. Human role: The original post does not say how a person stayed involved.; limitations: The source does not detail technical constraints, such as the specific environment used for PPO training or the complexity of the game's DOM/Canvas interaction. It is unclear if the agent generalizes to other games or if the score is reproducible. Human oversight requirements are not defined..
Tools
Published on Agent Work Atlas:
Overview
I used Grok to build the browser game Roofline.
Workflow
The author used Grok to develop a browser game named Roofline and subsequently trained a PPO agent to play the live page. The workflow involved generating the game code and then creating an agent script that interacts with the game interface. The source does not specify the exact human role in the training loop or debugging process.
Reported result
The agent achieved a best recorded run of 39,359 score, 4,924 meters, and a combo of 117. The game is hosted at httpx://roof.grok.me/. No further metrics on agent stability, training time, or failure rates are provided in the source.
Human role
The original post does not say how a person stayed involved.
Limitations
The source does not detail technical constraints, such as the specific environment used for PPO training or the complexity of the game's DOM/Canvas interaction. It is unclear if the agent generalizes to other games or if the score is reproducible. Human oversight requirements are not defined.
Try it
Act as a game development and AI agent engineer. I want to replicate the creation of a simple browser game and a PPO agent to play it. 1. **Game Creation**: Use Grok to generate a simple HTML5/JavaScript browser game (e.g., a runner or platformer) with clear scoring metrics (score, distance, combo). Save the code to [replaceable: local directory or GitHub repo]. 2. **Agent Setup**: Write a Python script using a reinforcement learning library (e.g., Stable Baselines3) to create a PPO agent. The agent must interact with the game via a headless browser (e.g., Playwright or Selenium) or a direct API if available. 3. **Training Loop**: Implement a training loop where the agent plays the game, receives rewards based on score/distance, and updates its policy. 4. **Verification**: Before running long training sessions, perform a dry run with 10 episodes to ensure the agent can read the game state and send inputs correctly. Show me the logs. 5. **Human Check**: Pause after the dry run for my confirmation before starting full training. Inputs: Game type preference [replaceable: e.g., 'endless runner'], target metrics [replaceable: e.g., 'maximize distance']. Output: Game code, agent training script, and a report of the best score achieved in the dry run. Constraints: Do not deploy to a public URL without my explicit approval. Ensure the agent does not crash the browser.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.