Update README.md

README.md

@@ -21,7 +21,7 @@ model-index:
       verified: false
 ---
 
-# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
+# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** 🚀
 This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
 and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
@@ -30,7 +30,7 @@ The RL Zoo is a training framework for Stable Baselines3
 reinforcement learning agents,
 with hyperparameter optimization and pre-trained agents included.
 
-## Usage (with SB3 RL Zoo)
+## Usage (with SB3 RL Zoo) 📚
 
 RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
 SB3: https://github.com/DLR-RM/stable-baselines3<br/>
@@ -54,14 +54,19 @@ python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -or
 python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4  -f logs/
 ```
 
-## Training (with the RL Zoo)
+## Training (with the RL Zoo) 🖥️
 ```
 python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
 # Upload the model and generate video (when possible)
 python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga LimTara
 ```
 
-## Hyperparameters
+## Hyperparameters 🔗
+Hyperparameters are used in order to control the behavior of the training model. 
+In this project, some of the hyperparameters include the number of times the model is simulated (n_timesteps). The higher the number, the more times the model is simulated and the higher the accuracy but the longer it takes to run. ⏰
+You can also control how risky it is willing to go in order to explore new things. The higher the risk, the more the model will explore and step out of their comfort zone (exploration_final_eps)! 💥
+
+To create and edit the hyperparameters, you need to create a file called "dqn.yml" by going to the left bar and click on the file icon which is right below the key icon. Then all you need to do is simply paste the following into the "dqn.yml" file! 📁 
 ```python
 OrderedDict([('batch_size', 32),
              ('buffer_size', 100000),