Peter images

8-Reinforcement/1-QLearning/README.md

@@ -22,6 +22,10 @@ You can open [the lesson notebook](notebook.ipynb) and walk through this lesson
 
 In this lesson, we will explore the world of **[Peter and the Wolf](https://en.wikipedia.org/wiki/Peter_and_the_Wolf)**, inspired by a musical fairy tale by a Russian composer, [Sergei Prokofiev](https://en.wikipedia.org/wiki/Sergei_Prokofiev). We will use **Reinforcement Learning** to let Peter explore his environment, collect tasty apples and avoid meeting the wolf.
 
+![peter and the wolf](images/peter.png)
+
+> 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**.
 
 ## The environment

8-Reinforcement/2-Gym/README.md

@@ -10,6 +10,10 @@ 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)
+
+> 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.
 
 <img alt="a cartpole" src="images/cartpole.png" width="200"/>