GamingAgent - Personal Computer Gaming Agent

Demos on X

Contents

• Gallery
• Introduction
• Installation
• APIs
• Games
  • Super Mario Bros 1985
  • 2048
  • Tetris

Gallery

🎥 Here you can see our AI gaming agents in action, demonstrating their gameplay strategies across different games!

Super Mario Bros AI Gameplay Comparison

AI Gameplays

2048 AI Gameplay Comparison

GPT-4o Gameplay	Claude-3.7 Gameplay

Tetris AI Gameplay

Claude-3.7 Gameplay

Introduction

The goal of this repo is to provide an easy solution of deploying computer use agents (CUAs) that run on your PC and laptops. As part of LMGames, our current focus is on building local gaming agents.

Current features:

• Gaming agents for Platformer and Atari games.

Installation

1. Clone this repository:

git clone https://github.com/lmgame-org/GamingAgent.git
cd GamingAgent

2. Install dependency:

conda create -n game_cua python==3.10 -y
pip install -r requirements.txt

APIs

Currently we support gaming agents based on the following models:

• OpenAI:
  • gpt-4o
  • gpt-4o-mini
  • o1
• Anthropic:
  • claude-3-5-sonnet-20241022
  • claude-3-7-sonnet-20250219
• Gemini:
  • gemini-1.5-pro

Set your API keys with:

export OPENAI_API_KEY={YOUR_OPENAI_API_KEY}
export ANTHROPIC_API_KEY={YOUR_ANTHROPIC_API_KEY}
export GEMINI_API_KEY={your_GEMINI_API_KEY}

⚠️ Due to concurrency, deploying the agent with high-end models (and a large number of workers) could incur higher cost.

Games

Super Mario Bros (1985 by Nintendo)

Game Installation

Install your Super Mario Bros game. In our demo, we adopt SuperMarioBros-C.

Navigate to the repo and follow the installation instructions.

Launch Gaming Agent

1. Once the game is built, download and move the ROM file:

mv path-to-your-ROM-file/"Super Mario Bros. (JU) (PRG0) [!].nes" $YOUR_WORKPLACE/SuperMarioBros-C/build/

2. Launch the game with

cd $YOUR_WORKPLACE/SuperMarioBros-C/build
./smbc

3. Full screen the game by pressing F. You should be able to see:

4. Open another screen, launch your agent in terminal with

cd $YOUR_WORKPLACE/GamingAgent
python games/superMario/mario_agent.py --api_provider {your_favorite_api_provider} --model_name {official_model_codename}

5. Due to concurrency issue, sometimes the agent will temporarily pause your game by pressing Enter. To avoid the issue, you can launch the agent only after entering the game upon seeing:

Other command options

--concurrency_interval: Interval in seconds between starting workers.

--api_response_latency_estimate: Estimated API response latency in seconds.

--policy: 'long', 'short', 'alternate' or 'mixed'. In 'long' or 'short' modes only those workers are enabled.

Build your own policy

You can implement your own policy in mario_agent.py! Deploying high-concurrency strategy with short-term planning streaming workers vs. low-concurrency strategy with long-term planning workers, or a mix of both.

In our early experiments, 'alternate' policy performs well. Try it yourself and find out which one works better!

2048

2048 is a sliding tile puzzle game where players merge numbered tiles to reach the highest possible value. In our demo, we adopt and modify 2048-Pygame

Game Set Up

Run the 2048 game with a defined window size:

python games/game_2048/game_logic.py -wd 600 -ht 600

Use Ctrl to restart the game and the arrow keys to move tiles strategically.

Start the AI agent to play automatically:

python games/game_2048/2048_agent.py

Other command options

--api_provider: API provider to use.

--model_name: Model name (has to come with vision capability).

Tetris

Game Installation

Install your Tetris game. In our demo, we adopt Python-Tetris-Game-Pygame.

Launch Gaming Agent

1. Launch the game with

cd $YOUR_WORKPLACE/Python-Tetris-Game-Pygame
python main.py

⚠️ In your Tetris implementation, Modify game speed to accomodate for AI gaming agent latency. For example, in the provided implementation, navigate to main.py, line 23: change event time to 500~600ms.

You should be able to see:

2. Adjust Agent's Field of Vision. Either full screen your game or adjust screen region in /games/tetris/workers.py, line 67 to capture only the gameplay window. For example, in Python-Tetris-Game-Pygame with MacBook Pro, change the line to region = (0, 0, screen_width // 32 * 9, screen_height // 32 * 20).

3. Open another screen, launch your agent in terminal with

cd $YOUR_WORKPLACE/GamingAgent
python games/tetris/tetris_agent.py

Other command options

--api_provider: API provider to use.

--model_name: Model name (has to come with vision capability).

--concurrency_interval: Interval in seconds between consecutive workers.

--api_response_latency_estimate: Estimated API response latency in seconds.

--policy: 'fixed', only one policy is supported for now.

Build your own policy

Currently we find single-worker agent is able to make meaningful progress in the Tetris game. If the gaming agent spawns multiple independent workers, they don't coordinate well. We will work on improving the agent and gaming policies. We also welcome your thoughts and contributions.