Update wording for SB3 tutorial (#1027)

docs/tutorials/sb3/connect_four.md

@@ -8,6 +8,7 @@ This tutorial shows how to train a agents using Maskable [Proximal Policy Optimi
 
 It creates a custom Wrapper to convert to a [Gymnasium](https://gymnasium.farama.org/)-like environment which is compatible with [SB3 action masking](https://sb3-contrib.readthedocs.io/en/master/modules/ppo_mask.html).
 
+After training and evaluation, this script will launch a demo game using human rendering. Trained models are saved and loaded from disk (see SB3's [documentation](https://stable-baselines3.readthedocs.io/en/master/guide/save_format.html) for more information).
 
 ```{eval-rst}
 .. note::
@@ -21,8 +22,6 @@ It creates a custom Wrapper to convert to a [Gymnasium](https://gymnasium.farama
     This wrapper assumes that the action space and observation space is the same for each agent, this assumption may not hold for custom environments.
 ```
 
-After training and evaluation, this script will launch a demo game using human rendering. Trained models are saved and loaded from disk (see SB3's [documentation](https://stable-baselines3.readthedocs.io/en/master/guide/save_format.html) for more information).
-
 
 ## Environment Setup
 To follow this tutorial, you will need to install the dependencies shown below. It is recommended to use a newly-created virtual environment to avoid dependency conflicts.

docs/tutorials/sb3/kaz.md

@@ -6,9 +6,9 @@ title: "SB3: PPO for Knights-Archers-Zombies"
 
 This tutorial shows how to train agents using [Proximal Policy Optimization](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html) (PPO) on the [Knights-Archers-Zombies](https://pettingzoo.farama.org/environments/butterfly/knights_archers_zombies/) environment ([AEC](https://pettingzoo.farama.org/api/aec/)).
 
-It converts the environment into a Parallel environment and uses SuperSuit to create vectorized environments, leveraging multithreading to speed up training.
+We use SuperSuit to create vectorized environments, leveraging multithreading to speed up training (see SB3's [vector environments documentation](https://stable-baselines3.readthedocs.io/en/master/guide/vec_envs.html)).
 
-After training and evaluation, this script will launch a demo game using human rendering. Trained models are saved and loaded from disk (see SB3's [documentation](https://stable-baselines3.readthedocs.io/en/master/guide/save_format.html) for more information).
+After training and evaluation, this script will launch a demo game using human rendering. Trained models are saved and loaded from disk (see SB3's [model saving documentation](https://stable-baselines3.readthedocs.io/en/master/guide/save_format.html)).
 
 ```{eval-rst}
 .. note::
@@ -17,9 +17,9 @@ After training and evaluation, this script will launch a demo game using human r
 ```
 
 ```{eval-rst}
-.. warning::
+.. note::
 
-    Because this environment allows agents to spawn and die, it requires using SuperSuit's Black Death wrapper, which provides blank observations to dead agents, rather than removing them from the environment.
+    This environment allows agents to spawn and die, so it requires using SuperSuit's Black Death wrapper, which provides blank observations to dead agents rather than removing them from the environment.
 ```
 
 

docs/tutorials/sb3/waterworld.md

@@ -6,8 +6,9 @@ title: "SB3: PPO for Waterworld (Parallel)"
 
 This tutorial shows how to train agents using [Proximal Policy Optimization](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html) (PPO) on the [Waterworld](https://pettingzoo.farama.org/environments/sisl/waterworld/) environment ([Parallel](https://pettingzoo.farama.org/api/parallel/)).
 
-After training and evaluation, this script will launch a demo game using human rendering. Trained models are saved and loaded from disk (see SB3's [documentation](https://stable-baselines3.readthedocs.io/en/master/guide/save_format.html) for more information).
+We use SuperSuit to create vectorized environments, leveraging multithreading to speed up training (see SB3's [vector environments documentation](https://stable-baselines3.readthedocs.io/en/master/guide/vec_envs.html)).
 
+After training and evaluation, this script will launch a demo game using human rendering. Trained models are saved and loaded from disk (see SB3's [model saving documentation](https://stable-baselines3.readthedocs.io/en/master/guide/save_format.html)).
 ```{eval-rst}
 .. note::