Fix discord link & typos

Author: AechPro

docs/Getting Started/introduction.md

@@ -7,28 +7,31 @@ sidebar_position: 1
 
 ## What is RLGym?
 
-RLGym is a Python API for creating reinforcement learning environments. While it was originally designed for the game [Rocket League](https://www.rocketleague.com), the core API is now game-agnostic. This means you can use RLGym to create any kind of environment you want, from simple grid worlds to complex physics simulations. Get an overview of how RLGym works in our [overview](/Getting%20Started/overview) section.
+RLGym is a Python API for creating reinforcement learning environments. While it was originally designed for the game [Rocket League](https://www.rocketleague.com), the core API is now game-agnostic. This means you can use RLGym to create any kind of environment you want, from simple grid worlds to complex physics simulations. Get an overview of the API in our [overview](/Getting%20Started/overview) section.
 
 ## How it Works
-RLGym provides a simple API for creating fully customizable environments for reinforcement learning projects. Each environment is built from a few core components, which we refer to as "configuration objects". When provided with a set of configuration objects, RLGym will handle the flow of information throughout the environment, and provide a simple interface for learning agents to interact with the environment.
+RLGym provides a simple API for creating fully customizable environments for reinforcement learning projects. Each environment is built from a few core components, which we refer to as "configuration objects". When provided with a set of configuration objects, RLGym will handle the flow of information throughout the environment, and provide a simple interface for learning agents to interact with.
 
 ## Getting Started
 
-The most developed use of RLGym is for Rocket League. We provide a complete environment implementation that allows users to train agents with [RocketSim](https://github.com/ZealanL/rocketsim), a headless simulator for Rocket League. Users can customize every aspect of the environment by implementing their own [Configuration Objects](/Getting%20Started/overview/), or use the default implementations provided by RLGym. Head over to our [Quick Start Guide](quickstart.md) if you want to jump right in to training a Rocket League agent, or check out our [Custom Environments](../../Custom%20Environments/custom-environment) section for a step-by-step guide to creating your own environment with the RLGym API.
+The most developed use of RLGym is for Rocket League. We provide a complete environment implementation that allows users to train agents with [RocketSim](https://github.com/ZealanL/rocketsim), a headless simulator for Rocket League. Users can customize every aspect of the environment by implementing their own [Configuration Objects](/Getting%20Started/overview/), or use the default implementations provided by RLGym. Head over to our [Quickstart Guide](quickstart.md) if you want to jump right in to training a Rocket League agent, or check out our [Custom Environments](../../Custom%20Environments/custom-environment) section for a step-by-step guide to creating your own environment with the RLGym API.
 
 ## Installation
 RLGym is split into several packages to keep things modular and lightweight. The core API package has no dependencies, while additional packages provide specific functionality:
 
 ```bash
-# Just the core API
-pip install rlgym
+# Installs every rlgym component
+pip install rlgym[all]  
 
-# Everything for Rocket League with RocketSim
-pip install rlgym[rl-sim]
+# Installs only the api
+pip install rlgym  
 
-# Everything for Rocket League with RocketSim and RLViser (visualization)
-pip install rlgym[rl-rlviser]
+# Installs all rocket league packages
+pip install rlgym[rl]  
 
-# All packages
-pip install rlgym[all]
+# Installs only RocketSim rocket league packages
+pip install rlgym[rl-sim]  
+
+# Installs RLViser and RocketSim rocket league packages
+pip install rlgym[rl-rlviser]  
 ```

docs/Getting Started/quickstart.md

@@ -105,4 +105,4 @@ if __name__ == "__main__":
 
 This code will train a 2v2 bot for 1 billion timesteps (That's over 18,500 hours of in-game time!), saving checkpoints every 1 million timesteps. You can stop the training process at any time by pressing P while the program is in focus.
 
-For a more complete guide for training your first agent, refer to our Rocket League tutorial on [training an agent](../Rocket%20League/training_an_agent).
+For a more complete guide to training your first agent, refer to our Rocket League tutorial on [training an agent](../Rocket%20League/training_an_agent).

docusaurus.config.js

@@ -120,7 +120,7 @@ const config = {
             items: [
               {
                 label: 'Discord',
-                href: 'https://discord.gg/NjAHcP32Ae',
+                href: 'https://discord.gg/E6CDtwgP8F',
                 type: 'discord',
               },
               {