diff --git a/README.cn.md b/README.cn.md
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 PARL的目标是构建一个可以完成复杂任务的智能体。以下是用户在逐步构建一个智能体的过程中需要了解到的结构：
 
 ### Model
-`Model` 用来定义前向(`Forward`)网络，这通常是一个策略网络(`Policy Network`)或者一个值函数网络(`Value Function`)，输入是当前环境状态(`State`)。
+`Model` 用来定义前向 (`Forward`)网络，这通常是一个策略网络 (`Policy Network`)或者一个值函数网络 (`Value Function`)，输入是当前环境状态 (`State`)。
 
 ### Algorithm
-`Algorithm` 定义了具体的算法来更新前向网络(`Model`)，也就是通过定义损失函数来更新`Model`。一个`Algorithm`包含至少一个`Model`。
+`Algorithm` 定义了具体的算法来更新前向网络 (`Model`)，也就是通过定义损失函数来更新`Model`。一个`Algorithm`包含至少一个`Model`。
 
 ### Agent
-`Agent` 负责算法与环境的交互，在交互过程中把生成的数据提供给`Algorithm`来更新模型(`Model`)，数据的预处理流程也一般定义在这里。
+`Agent` 负责算法与环境的交互，在交互过程中把生成的数据提供给`Algorithm`来更新模型 (`Model`)，数据的预处理流程也一般定义在这里。
 
 提示： 请访问[教程](https://parl.readthedocs.io/en/latest/getting_started.html) and [API 文档](https://parl.readthedocs.io/en/latest/model.html)以获取更多关于基础类的信息。
 
diff --git a/README.md b/README.md
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@@ -57,7 +57,7 @@ Two steps to use outer computation resources:
 2. call `parl.connect` to initialize parallel communication before creating an object. Calling any function of the objects **does not** consume local computation resources since they are executed elsewhere.
 
 <img src=".github/decorator.png" alt="PARL" width="450"/>
-As shown in the above figure, real actors(orange circle) are running at the cpu cluster, while the learner(blue circle) is running at the local gpu with several remote actors(yellow circle with dotted edge).  
+As shown in the above figure, real actors (orange circle) are running at the cpu cluster, while the learner (blue circle) is running at the local gpu with several remote actors (yellow circle with dotted edge).  
 
 For users, they can write code in a simple way, just like writing multi-thread code, but with actors consuming remote resources. We have also provided examples of parallized algorithms like [IMPALA](examples/IMPALA), [A2C](examples/A2C) and [GA3C](examples/GA3C). For more details in usage please refer to these examples.