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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
This is an AI agent for Street Fighter II Champion Edition.
| Date | Stars |
|---|---|
| 2026-07-31 | 6526 |
| 2026-08-02 | 6525 |
| 2026-08-06 | 6525 |
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# SFighterAI [简体中文](README_CN.md) | English | [Español](README_ES.md) This project is an AI agent trained using deep reinforcement learning to beat the final boss in the game "Street Fighter II: Special Champion Edition". The AI agent makes decisions based solely on the game screen's RGB pixel values. In the provided save state, the agent achieves a 100% win rate in the first round of the final level (overfitting occurs, see the [Running Tests](#running-tests) section for discussion). ### File Structure ```bash ├───data ├───main │ ├───logs │ ├───trained_models │ └───scripts ├───utils │ └───scripts ``` The game configuration files are stored in the `data/` folder, and the main project code is in the `main/` folder. Within `main/`, the `logs/` folder contains terminal/console outputs and data curves recording the training process (viewable with Tensorboard), while the `trained_models/` folder contains model weights from different stages. These weights can be used for running tests in `test.py` to observe the performance of the AI agent's learned strategies at different training stages. ## Running Guide This project is based on the Python programming language and primarily utilizes standard libraries like [OpenAI Gym Retro](https://retro.readthedocs.io/en/latest/getting_started.html) and [Stable-Baselines3](https://stable-baselines3.readthedocs.io/en/master/). The Python version used is 3.8.10, and it is recommended to use [Anaconda](https://www.anaconda.com) to configure the Python environment. The following setup process has been tested on Windows 11. Below are console/terminal/shell commands. ### Environment Setup ```bash # Create a conda environment named StreetFighterAI with Python version 3.8.10 conda create -n StreetFighterAI python=3.8.10 conda activate StreetFighterAI # Install Python libraries cd [parent_directory_of_project]/street-fighter-ai/main pip install -r requirements.txt # Run script to locate gym-retro game folder cd .. python .\utils\print_game_lib_folder.py ``` After the console outputs the folder path, copy it to the file explorer and navigate to the corresponding path. This folder contains the game data files for "Street Fighter II: Special Champion Edition" within gym-retro, including the game ROM file and data configuration files. Copy the `Champion.Level12.RyuVsBison.state`, `data.json`, `metadata.json`, and `scenario.json` files from the `data/` folder of this project into the game data folder, replacing the original files (administrator privileges may be required). The `.state` file is a save state for the game's highest difficulty level, while the three `.json` files are gym-retro configuration files storing game information memory addresses (this project only uses [agent_hp] and [enemy_hp] for reading character health values in real-time). To run the program, you will also need the game ROM file for "Street Fighter II: Special Champion Edition", which is not provided by gym-retro and must be obtained legally through other means. You can refer to this [link](https://wowroms.com/en/roms/sega-genesis-megadrive/street-fighter-ii-special-champion-edition-europe/26496.html). Once you have legally obtained the game ROM file, copy it to the aforementioned gym-retro game data folder and rename it to `rom.md`. At this point, the environment setup is complete. Note 1: If you want to manually capture save states and find memory variables in the game, you can use the gym-retro integration ui. Copy `data/Gym Retro Integration.exe` to the parent menu (two levels up, `retro/` folder) of the aforementioned gym-retro game data folder. Note 2: If you want to record videos of the AI agent's gameplay, you will need to install [ffmpeg](https://ffmpeg.org/). ```bash conda install ffmpeg ``` ### <a name="running-tests"></a>Running Tests Once the environment is set up, you can run `test.py` in the `main/` folder to test and experience the AI agent's performance at different stages of training. ``
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b8ac1de82741d6fe, desc:ai agent