From 902aec400230ad82af6c4a8b9ace3282d981da7e Mon Sep 17 00:00:00 2001
From: linyiLYi <48440925+linyiLYi@users.noreply.github.com>
Date: Fri, 7 Apr 2023 09:40:52 +0800
Subject: [PATCH] Update  README (add jumping anchor)

---
 README.md    | 6 +++---
 README_CN.md | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index c744b46..ff06671 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
 
 [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).
+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
 
@@ -16,7 +16,7 @@ This project is an AI agent trained using deep reinforcement learning to beat th
 │   └───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.
+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
 
@@ -50,7 +50,7 @@ Note: If you want to record videos of the AI agent's gameplay, you will need to
 conda install ffmpeg
 ```
 
-### Running Tests
+### <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.
 
diff --git a/README_CN.md b/README_CN.md
index 66d235b..fbba4a2 100644
--- a/README_CN.md
+++ b/README_CN.md
@@ -2,7 +2,7 @@
 
 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）。
+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](#running-tests)部分的讨论）。
 
 ### 文件结构
 
@@ -49,7 +49,7 @@ python .\utils\print_game_lib_folder.py
 conda install ffmpeg
 ```
 
-### 运行测试
+### <a name="running-tests"></a>运行测试
 
 环境配置完成后，可以在 `main/` 文件夹下运行 `test.py` 进行测试，实际体验智能代理在不同训练阶段的表现。