diff --git a/8-Reinforcement/2-Gym/README.md b/8-Reinforcement/2-Gym/README.md index 6d272116ed..c3c0cd9472 100644 --- a/8-Reinforcement/2-Gym/README.md +++ b/8-Reinforcement/2-Gym/README.md @@ -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): diff --git a/8-Reinforcement/2-Gym/assignment.md b/8-Reinforcement/2-Gym/assignment.md index 9bffaa7c47..603c79daaf 100644 --- a/8-Reinforcement/2-Gym/assignment.md +++ b/8-Reinforcement/2-Gym/assignment.md @@ -1,10 +1,10 @@ # Train Mountain Car -[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: