Update English version of README

README_CN.md

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 # SFighterAI
 
+简体中文 | [English](README.md)
+
 本项目基于深度强化学习训练了一个用于通关《街头霸王·二：冠军特别版》（Street Fighter II Special Champion Edition）关底 BOSS 的智能 AI 代理。该智能代理完全基于游戏画面（RGB 像素值）进行决策，在该项目给定存档中最后一关的第一轮对局可以取得 100% 胜率（实际上出现了“过拟合”现象，详见[运行测试]部分的讨论）。
 
 ### 文件结构
@@ -18,7 +20,7 @@
 
 ## 运行指南
 
-本项目基于 Python 编程语言，主要使用了 [OpenAI Gym Retro](https://retro.readthedocs.io/en/latest/getting_started.html)、[Stable-Baselines3](https://stable-baselines3.readthedocs.io/en/master/) 等标准代码库。程序运行使用的 Python 版本为 3.8.10，建议使用 [Anaconda](https://www.anaconda.com) 配置 Python 环境。以下配置过程已在 Windows 11 系统上测试通过。以下为控制台/终端（Console/Terminal/Shell）指令：
+本项目基于 Python 编程语言，主要使用了 [OpenAI Gym Retro](https://retro.readthedocs.io/en/latest/getting_started.html)、[Stable-Baselines3](https://stable-baselines3.readthedocs.io/en/master/) 等标准代码库。程序运行使用的 Python 版本为 3.8.10，建议使用 [Anaconda](https://www.anaconda.com) 配置 Python 环境。以下配置过程已在 Windows 11 系统上测试通过。以下为控制台/终端（Console/Terminal/Shell）指令。
 
 ### 环境配置
 

main/README

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+# SFighterAI
+
+[简体中文](README_CN.md) | English
+
+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] 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 texts 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: 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
+```
+
+### 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.
+
+```bash
+cd [parent_directory_of_project]/street-fighter-ai/main
+python test.py
+```
+
+Model weight files are stored in the `main/trained_models/` folder. The default model used in `test.py` is `ppo_ryu_2500000_steps_updated.zip`, which has good generalization and is capable of beating the final level of Street Fighter II: Special Champion Edition. If you want to see the performance of other models, you can change the `model_path` variable in `test.py` to the path of another model file. The observed performance of the models at various training stages is as follows:
+
+* ppo_ryu_2000000_steps_updated: Just beginning to overfit state, generalizable but not quite capable.
+* ppo_ryu_2500000_steps_updated: Approaching the final overfitted state, cannot dominate first round but partially generalizable. High chance of beating the final stage.
+* ppo_ryu_3000000_steps_updated: Near the final overfitted state, almost dominate first round but barely generalizable.
+* ppo_ryu_7000000_steps_updated: Overfitted, dominates first round but not generalizable. 
+
+### Training the Model
+
+If you want to train your own model, you can run `train.py` in the `main/` folder.
+
+```bash
+cd [parent_directory_of_project]/street-fighter-ai/main
+python train.py
+```
+
+### Viewing Training Curves
+
+The project includes Tensorboard graphs of the training process. You can use Tensorboard to view detailed data. It is recommended to use the integrated Tensorboard plugin in VSCode to view the data directly. The traditional viewing method is listed below:
+
+```bash
+cd [parent_directory_of_project]/street-fighter-ai/main
+tensorboard --logdir=logs/
+```
+
+Open the default Tensorboard service address `http://localhost:6006/` in your browser to view interactive graphs of the training process.
+
+## Acknowledgements
+This project uses open-source libraries such as [OpenAI Gym Retro](https://retro.readthedocs.io/en/latest/getting_started.html)、[Stable-Baselines3](https://stable-baselines3.readthedocs.io/en/master/). The contributions of all the developers to the open-source community are appreciated!
+
+Two papers that had a significant impact on this project:
+
+[1] [DIAMBRA Arena A New Reinforcement Learning Platform for Research and Experimentation](https://arxiv.org/abs/2210.10595)
+The valuable summary of the experience in setting hyperparameters for deep reinforcement learning models in fighting games in this paper was of great help to the training process of this project.
+
+[2] [Mitigating Cowardice for Reinforcement Learning](https://ieee-cog.org/2022/assets/papers/paper_111.pdf)
+The "penalty decay" mechanism proposed in this paper effectively solved the "cowardice" problem (always avoiding opponents and not daring to even try attacking moves).