@ -24,7 +24,7 @@ In this lesson, we will explore the world of **[Peter and the Wolf](https://en.w
> 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**.
**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**.
@ -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.
> **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.
> 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.
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.
Jen Looper
4 years ago
