diff --git a/8-Reinforcement/1-QLearning/README.md b/8-Reinforcement/1-QLearning/README.md
index 57aa6953..d0c13ce7 100644
--- a/8-Reinforcement/1-QLearning/README.md
+++ b/8-Reinforcement/1-QLearning/README.md
@@ -24,7 +24,7 @@ In this lesson, we will explore the world of **[Peter and the Wolf](https://en.w
 
 ![peter and the wolf](images/peter.png)
 
-> Image by [Jen Looper](https://twitter.com/jenlooper)
+> Peter and his friends need to escape the hungry wolf! Image by [Jen Looper](https://twitter.com/jenlooper)
 
 **Reinforcement Learning** (RL) is a learning technique that allows us to learn an optimal behavior of an **agent** in some **environment** by running many experiments. An agent in this environment should have some **goal**, defined by a **reward function**.
 
diff --git a/8-Reinforcement/2-Gym/README.md b/8-Reinforcement/2-Gym/README.md
index 3b7b99b4..b1a2e2a2 100644
--- a/8-Reinforcement/2-Gym/README.md
+++ b/8-Reinforcement/2-Gym/README.md
@@ -10,9 +10,9 @@ In this lesson we will apply the same principles of Q-Learning to a problem with
 
 > **Problem**: If Peter wants to escape from the wolf, he needs to be able to move faster. We will see how Peter can learn to skate, in particular, to keep balance, using Q-Learning.
 
-![skating](images/skate.png)
+![The great escape!](images/escape.png)
 
-> Image by [Jen Looper](https://twitter.com/jenlooper)
+> Peter and his friends get creative to escape the wolf! Image by [Jen Looper](https://twitter.com/jenlooper)
 
 We will use a simplified version of balancing known as a **CartPole** problem. In the cartpole world, we have a horizontal slider that can move left or right, and the goal is to balance a vertical pole on top of the slider.
 
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