@ -24,9 +24,9 @@ In this lesson, we will be using a library called **OpenAI Gym** to simulate dif
## 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 **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.
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://gymnasium.farama.org/), 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).
> **Note**: You can see other environments available from OpenAI Gym [here](https://gymnasium.farama.org/environments/classic_control/).
First, let's install the gym and import required libraries (code block 1):
[OpenAI Gym](http://gym.openai.com) has been designed in such a way that all environments provide the same API - i.e. the same methods `reset`, `step` and `render`, and the same abstractions of **action space** and **observation space**. Thus is should be possible to adapt the same reinforcement learning algorithms to different environments with minimal code changes.
[OpenAI Gym](https://gymnasium.farama.org) has been designed in such a way that all environments provide the same API - i.e. the same methods `reset`, `step` and `render`, and the same abstractions of **action space** and **observation space**. Thus is should be possible to adapt the same reinforcement learning algorithms to different environments with minimal code changes.
## A Mountain Car Environment
[Mountain Car environment](https://gym.openai.com/envs/MountainCar-v0/) contains a car stuck in a valley:
[Mountain Car environment](https://gymnasium.farama.org/environments/classic_control/mountain_car/) contains a car stuck in a valley:
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