@ -17,7 +17,7 @@ We will use a simplified version of balancing known as a **CartPole** problem. I
In this lesson, we will be using a library called **OpenAI Gym** to simulate different **environments**. You can run this lesson's code locally (eg. from Visual Studio Code), in which case the simulation will open in a new window. When running the code online, you may need to make some tweaks to the code, as described [here](https://towardsdatascience.com/rendering-openai-gym-envs-on-binder-and-google-colab-536f99391cc7).
## OpenAI Gym
In the previous lesson, the rules of the game and the state were given by the `Board` class which we defined ourselves. Here we will use a special **sumulation environment**, which will simulate the physics behind the balancing pole. One of the most popular simulation environments for training reinforcement learning algorithms is called a [Gym](https://gym.openai.com/), which is maintained by [OpenAI](https://openai.com/). By using this gym we can create difference **environments** from a cartpole simulation to Atari games.
In the previous lesson, the rules of the game and the state were given by the `Board` class which we defined ourselves. Here we will use a special **simulation environment**, which will simulate the physics behind the balancing pole. One of the most popular simulation environments for training reinforcement learning algorithms is called a [Gym](https://gym.openai.com/), which is maintained by [OpenAI](https://openai.com/). By using this gym we can create difference **environments** from a cartpole simulation to Atari games.
> **Note**: You can see other environments available from OpenAI Gym [here](https://gym.openai.com/envs/#classic_control).
Jen Looper
3 years ago