docs change

docs/02_notebooks/L0_overview.ipynb

@@ -12,7 +12,7 @@
    },
    "source": [
     "# Overview\n",
-    "Before we get started, we must first install Tianshou's library and Gym environment by running the commands below. This tutorials will always keep up with the latest version of Tianshou since they also serve as a test for the latest version. If you are using an older version of Tianshou, please refer to the [documentation](https://tianshou.readthedocs.io/en/latest/) of your version.\n"
+    "To begin, ensure you have Tianshou and the Gym environment installed by executing the following commands. This tutorials will always keep up with the latest version of Tianshou since they also serve as a test for the latest version. For users on older versions of Tianshou, please consult the [documentation](https://tianshou.readthedocs.io/en/latest/) corresponding to your version..\n"
    ]
   },
   {

docs/02_notebooks/L1_Batch.ipynb

@@ -7,7 +7,7 @@
    },
    "source": [
     "# Batch\n",
-    "In this tutorial, we will introduce the **Batch** to you, which is the most basic data structure in Tianshou. You can consider Batch as a numpy version of python dictionary. It is also similar to pytorch's tensordict,\n",
+    "In this tutorial, we will introduce the **Batch** to you, which serves as the fundamental data structure in Tianshou. Think of Batch as a numpy-enhanced version of a Python dictionary. It is also similar to pytorch's tensordict,\n",
     "although with a somehow different type structure."
    ]
   },
@@ -62,7 +62,7 @@
     "\n",
     "## Why do we need Batch in Tianshou?\n",
     "The motivation behind the implementation of Batch module is simple. In DRL, you need to handle a lot of dictionary-format data. For instance, most algorithms would require you to store state, action, and reward data for every step when interacting with the environment. All of them can be organized as a dictionary, and the\n",
-    " Batch class helps Tianshou in unifying the interfaces of a diverse set of algorithms. In addition, Batch supports advanced indexing, concatenation and splitting, formatting print just like any other numpy array, which proved to be helpful for developers.\n",
+    " Batch class helps Tianshou in unifying the interfaces of a diverse set of algorithms. In addition, Batch supports advanced indexing, concatenation, and splitting, as well as printing formatted outputs akin to standard numpy arrays, proving invaluable for developers.\n",
     "<div align=center>\n",
     "<img src=\"https://tianshou.readthedocs.io/en/master/_images/concepts_arch.png\", title=\"The data flow is converted into a Batch in Tianshou\">\n",
     "\n",
@@ -145,7 +145,7 @@
    },
    "source": [
     "### Getting access to data\n",
-    "You can conveniently search or change the key-value pair in a Batch just as if it were a python dictionary."
+    "You can effortlessly search for or modify key-value pairs within a Batch, much like interacting with a Python dictionary."
    ]
   },
   {