pratikgx · Sep 28, 2026
diff --git a/units/en/unit1/additional-readings.mdx b/units/en/unit1/additional-readings.mdx
@@ -11,4 +11,4 @@ These are **optional readings** if you want to go deeper.
 ## Gym [[gym]]
 
 - [Getting Started With OpenAI Gym: The Basic Building Blocks](https://blog.paperspace.com/getting-started-with-openai-gym/)
-- [Make your own Gym custom environment](https://www.gymlibrary.dev/content/environment_creation/)
+- [Make your own Gym custom environment](https://gymnasium.farama.org/introduction/create_custom_env/)
diff --git a/units/en/unit1/hands-on.mdx b/units/en/unit1/hands-on.mdx
@@ -680,7 +680,7 @@ Here are some ideas to achieve so:
 
 **Compare the results of your LunarLander-v2 with your classmates** using the [leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) 🏆
 
-Is moon landing too boring for you? Try to **change the environment**, why not use MountainCar-v0, CartPole-v1 or CarRacing-v0? Check how they work [using the gym documentation](https://www.gymlibrary.dev/) and have fun 🎉.
+Is moon landing too boring for you? Try to **change the environment**, why not use MountainCar-v0, CartPole-v1 or CarRacing-v0? Check how they work [using the gym documentation](https://gymnasium.farama.org/) and have fun 🎉.
 
 ________________________________________________________________________
 Congrats on finishing this chapter! That was the biggest one, **and there was a lot of information.**

diff --git a/units/en/unit2/hands-on.mdx b/units/en/unit2/hands-on.mdx
@@ -190,7 +190,7 @@ We're now ready to code our Q-Learning algorithm 🔥
 
 # Part 1: Frozen Lake ⛄ (non slippery version)
 
-## Create and understand [FrozenLake environment ⛄]((https://gymnasium.farama.org/environments/toy_text/frozen_lake/)
+## Create and understand [FrozenLake environment ⛄](https://gymnasium.farama.org/environments/toy_text/frozen_lake/)
 ---
 
 💡 A good habit when you start to use an environment is to check its documentation
@@ -572,7 +572,7 @@ def evaluate_agent(env, max_steps, n_eval_episodes, Q, seed):
 ## Evaluate our Q-Learning agent 📈
 
 - Usually, you should have a mean reward of 1.0
-- The **environment is relatively easy** since the state space is really small (16). What you can try to do is [to replace it with the slippery version](https://www.gymlibrary.dev/environments/toy_text/frozen_lake/), which introduces stochasticity, making the environment more complex.
+- The **environment is relatively easy** since the state space is really small (16). What you can try to do is [to replace it with the slippery version](https://gymnasium.farama.org/environments/toy_text/frozen_lake/), which introduces stochasticity, making the environment more complex.
 
 ```python
 # Evaluate our Agent

diff --git a/units/en/unit4/hands-on.mdx b/units/en/unit4/hands-on.mdx
@@ -63,7 +63,7 @@ To test its robustness, we're going to train it in 2 different simple environmen
 
 ### 🎮 Environments:
 
-- [CartPole-v1](https://www.gymlibrary.dev/environments/classic_control/cart_pole/)
+- [CartPole-v1](https://gymnasium.farama.org/environments/classic_control/cart_pole/)
 - [PixelCopter](https://pygame-learning-environment.readthedocs.io/en/latest/user/games/pixelcopter.html)
 
 ### 📚 RL-Library:
@@ -205,7 +205,7 @@ We're now ready to implement our Reinforce algorithm 🔥
 
 ## Create the CartPole environment and understand how it works
 
-### [The environment 🎮](https://www.gymlibrary.dev/environments/classic_control/cart_pole/)
+### [The environment 🎮](https://gymnasium.farama.org/environments/classic_control/cart_pole/)
 
 ### Why do we use a simple environment like CartPole-v1?
 

diff --git a/units/en/unit5/conclusion.mdx b/units/en/unit5/conclusion.mdx
@@ -2,7 +2,7 @@
 
 Congrats on finishing this unit! You’ve just trained your first ML-Agents and shared it to the Hub 🥳.
 
-The best way to learn is to **practice and try stuff**. Why not try another environment? [ML-Agents has 18 different environments](https://github.com/Unity-Technologies/ml-agents/blob/develop/docs/Learning-Environment-Examples.md).
+The best way to learn is to **practice and try stuff**. Why not try another environment? [ML-Agents has 18 different environments](https://github.com/Unity-Technologies/ml-agents/blob/develop/com.unity.ml-agents/Documentation~/Learning-Environment-Examples.md).
 
 For instance:
 - [Worm](https://singularite.itch.io/worm), where you teach a worm to crawl.

diff --git a/units/en/unit7/hands-on.mdx b/units/en/unit7/hands-on.mdx
@@ -99,7 +99,7 @@ Mac: Download [this executable](https://drive.google.com/drive/folders/1h7YB0qwj
 
 ## Step 1: Understand the environment
 
-The environment is called `SoccerTwos`. The Unity MLAgents Team made it. You can find its documentation [here](https://github.com/Unity-Technologies/ml-agents/blob/develop/docs/Learning-Environment-Examples.md#soccer-twos)
+The environment is called `SoccerTwos`. The Unity MLAgents Team made it. You can find its documentation [here](https://github.com/Unity-Technologies/ml-agents/blob/develop/com.unity.ml-agents/Documentation~/Learning-Environment-Examples.md#soccer-twos)
 
 The goal in this environment **is to get the ball into the opponent's goal while preventing the ball from entering your own goal.**
 

diff --git a/units/en/unit8/hands-on-cleanrl.mdx b/units/en/unit8/hands-on-cleanrl.mdx
@@ -18,7 +18,7 @@ So, to be able to code it, we're going to use two resources:
 - In addition to the tutorial, to go deeper, you can read the 13 core implementation details: [https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/](https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/)
 
 Then, to test its robustness, we're going to train it in:
-- [LunarLander-v2](https://www.gymlibrary.ml/environments/box2d/lunar_lander/)
+- [LunarLander-v2](https://gymnasium.farama.org/environments/box2d/lunar_lander/)
 
 <figure class="image table text-center m-0 w-full">
     <video
@@ -51,7 +51,7 @@ In this notebook, you'll learn to **code your PPO agent from scratch with PyTorc
 
 To test its robustness, we're going to train it in:
 
-- [LunarLander-v2 🚀](https://www.gymlibrary.dev/environments/box2d/lunar_lander/)
+- [LunarLander-v2 🚀](https://gymnasium.farama.org/environments/box2d/lunar_lander/)
 
 We're constantly trying to improve our tutorials, so **if you find some issues in this notebook**, please [open an issue on the GitHub Repo](https://github.com/huggingface/deep-rl-class/issues).
 

diff --git a/units/en/unit8/hands-on-sf.mdx b/units/en/unit8/hands-on-sf.mdx
@@ -119,7 +119,7 @@ The library includes feature such as:
 
 - Multi-platform (Linux, macOS, Windows),
 - API for Python and C++,
-- [OpenAI Gym](https://www.gymlibrary.dev/) environment wrappers
+- [OpenAI Gym](https://gymnasium.farama.org/) environment wrappers
 - Easy-to-create custom scenarios (visual editors, scripting language, and examples available),
 - Async and sync single-player and multiplayer modes,
 - Lightweight (few MBs) and fast (up to 7000 fps in sync mode, single-threaded),
@@ -135,11 +135,11 @@ The library includes feature such as:
 
 Now that our Colab runtime is set up, we can start by installing the dependencies required to run ViZDoom on linux.
 
-If you are following on your machine on Mac, you will want to follow the installation instructions on the [github page](https://github.com/Farama-Foundation/ViZDoom/blob/master/doc/Quickstart.md#-quickstart-for-macos-and-anaconda3-python-36).
+If you are following on your machine on Mac, you will want to follow the installation instructions on the [github page](https://vizdoom.farama.org/introduction/python_quickstart/#-quickstart-for-macos-and-anaconda3-python-36).
 
 ```python
 # Install ViZDoom deps from
-# https://github.com/mwydmuch/ViZDoom/blob/master/doc/Building.md#-linux
+# https://vizdoom.farama.org/dev/building_from_source/#-linux
 
 apt-get install build-essential zlib1g-dev libsdl2-dev libjpeg-dev \
 nasm tar libbz2-dev libgtk2.0-dev cmake git libfluidsynth-dev libgme-dev \

diff --git a/units/en/unitbonus3/envs-to-try.mdx b/units/en/unitbonus3/envs-to-try.mdx
@@ -9,7 +9,7 @@ Here we provide a list of interesting environments you can try to train your age
 
 DIAMBRA Arena is a software package featuring a collection of high-quality environments for Reinforcement Learning research and experimentation. It provides a standard interface to popular arcade emulated video games, offering a Python API fully compliant with OpenAI Gym/Gymnasium format, that makes its adoption smooth and straightforward.
 
-It supports all major Operating Systems (Linux, Windows and MacOS) and can be easily installed via [Python PIP](https://pypi.org/project/diambra-arena/). It is completely free to use, the user only needs to register on the [official website](https://diambra.ai/register/).
+It supports all major Operating Systems (Linux, Windows and MacOS) and can be easily installed via [Python PIP](https://pypi.org/project/diambra-arena/). It is completely free to use, the user only needs to register on the [official website](https://diambra.ai/).
 
 In addition, its [GitHub repository](https://github.com/diambra/) provides a collection of examples covering main use cases of interest that can be run in just a few steps.
 
@@ -39,7 +39,7 @@ To start using this environment, check these resources:
 - [Official Docs](https://docs.diambra.ai/)
 - [Competition Platform](https://diambra.ai)
 - [GitHub](https://github.com/diambra/)
-- [Discord](https://diambra.ai/discord)
+- [Discord](https://discord.gg/diambra)
 
 ## MineRL
 

diff --git a/units/en/unitbonus3/godotrl.mdx b/units/en/unitbonus3/godotrl.mdx
@@ -5,7 +5,7 @@
 The library provides:
 
 - An interface between games created in the [Godot Engine](https://godotengine.org/) and Machine Learning algorithms running in Python
-- Wrappers for four well known rl frameworks: [StableBaselines3](https://stable-baselines3.readthedocs.io/en/master/), [CleanRL](https://docs.cleanrl.dev/), [Sample Factory](https://www.samplefactory.dev/) and [Ray RLLib](https://docs.ray.io/en/latest/rllib-algorithms.html)
+- Wrappers for four well known rl frameworks: [StableBaselines3](https://stable-baselines3.readthedocs.io/en/master/), [CleanRL](https://docs.cleanrl.dev/), [Sample Factory](https://www.samplefactory.dev/) and [Ray RLLib](https://docs.ray.io/en/latest/rllib/rllib-algorithms.html)
 - Support for memory-based agents with LSTM or attention based interfaces
 - Support for *2D and 3D games*
 - A suite of *AI sensors* to augment your agent's capacity to observe the game world
@@ -37,9 +37,9 @@ In order to create games in Godot, **you must first download the editor**. Godot
 
 Which can be downloaded at the following links:
 
-- [Windows](https://downloads.tuxfamily.org/godotengine/4.0.1/Godot_v4.0.1-stable_win64.exe.zip)
-- [Mac](https://downloads.tuxfamily.org/godotengine/4.0.1/Godot_v4.0.1-stable_macos.universal.zip)
-- [Linux](https://downloads.tuxfamily.org/godotengine/4.0.1/Godot_v4.0.1-stable_linux.x86_64.zip)
+- [Windows](https://github.com/godotengine/godot-builds/releases/download/4.0.1-stable/Godot_v4.0.1-stable_win64.exe.zip)
+- [Mac](https://github.com/godotengine/godot-builds/releases/download/4.0.1-stable/Godot_v4.0.1-stable_macos.universal.zip)
+- [Linux](https://github.com/godotengine/godot-builds/releases/download/4.0.1-stable/Godot_v4.0.1-stable_linux.x86_64.zip)
 
 ### Loading the starter project