Update README.md

README.md

@@ -22,13 +22,11 @@ model-index:
 ---
 
 # **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).
+Imagine you have a robot friend who loves playing a game called Space Invaders. We've taught this robot how to play the game really well using a special technique called Deep Q-Network (DQN). The robot can now play Space Invaders without missing a single frame!
 
-The RL Zoo is a training framework for Stable Baselines3
-reinforcement learning agents,
-with hyperparameter optimization and pre-trained agents included.
+We used a special library called Stable Baselines3 to help teach the robot how to play. It's like a school for robots to learn cool things. And to make things even easier, we have a special place called the RL Zoo where the robot can practice and get even better at playing the game.
+
+So, what you're seeing is a video of our robot friend playing Space Invaders like a pro, thanks to the training it received using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). It's like watching a robot master a game after lots of practice and learning in a special training ground!
 
 ## Usage (with SB3 RL Zoo) 📚
 
@@ -55,6 +53,7 @@ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4  -f logs/
 ```
 
 ## Training (with the RL Zoo) 👾
+The code segment provided is a set of commands used to train a DQN agent on the SpaceInvaders game environment, store the training logs, and then upload the trained model to a specific organization repository, potentially generating a video in the process.
 ```
 python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
 # Upload the model and generate video (when possible)