Added Installation to Docs (#393)

docs/content/basic_usage.md

@@ -9,17 +9,28 @@ firstpage:
 
 There is a UI application which allows you to manually control the agent with the arrow keys:
 
-```
+```bash
 ./minigrid/manual_control.py
 ```
 
 The environment being run can be selected with the `--env` option, eg:
 
-```
+```bash
 ./minigrid/manual_control.py --env MiniGrid-Empty-8x8-v0
 ```
 
-## Training an Agent
+## Installation
+
+Minigrid call be installed via `pip`:
 
-If you want to train an agent with reinforcement learning, please refer to the [training tutorial](training.md).
+```bash
+python3 -m pip install minigrid
+```
+
+To modify the codebase or contribute to Minigrid, you would need to install Minigrid from source:
 
+```bash
+git clone https://github.com/Farama-Foundation/Minigrid.git
+cd Minigrid
+python3 -m pip install -e .
+```

docs/content/training.md

@@ -10,7 +10,7 @@ The environments in the Minigrid library can be trained easily using [StableBase
 
 ## Create Custom Feature Extractor 
 
-Although `StableBaselines3` is fully compatible with `Gymnasium`-based environments, including Minigrid, the default CNN architecture does not directly support the Minigrid observation space. Thus, to train an agent on Minigrid environments, we therefore need to create a custom feature extractor. This can be done by creating a feature extractor class that inherits from `stable_baselines3.common.torch_layers.BaseFeaturesExtractor`
+Although `StableBaselines3` is fully compatible with `Gymnasium`-based environments (which includes Minigrid), the default CNN architecture does not directly support the Minigrid observation space. Thus, to train an agent on Minigrid environments, we need to create a custom feature extractor. This can be done by creating a feature extractor class that inherits from `stable_baselines3.common.torch_layers.BaseFeaturesExtractor`
 
 ```python
 class MinigridFeaturesExtractor(BaseFeaturesExtractor):