Merge branch 'Farama-Foundation:main' into py313

docs/environments/third_party_environments.md

@@ -48,6 +48,13 @@ goal-RL ([Gymnasium-Robotics](https://robotics.farama.org/)).
 ### Autonomous Driving environments
 *Autonomous Vehicle and traffic management.*
 
+- [BlueSky-Gym: Reinforcement Learning Environments for Air Traffic Applications](https://github.com/TUDelft-CNS-ATM/bluesky-gym)
+
+  ![Gymnasium version dependency](https://img.shields.io/badge/Gymnasium-v0.28.1-blue)
+  ![GitHub stars](https://img.shields.io/github/stars/TUDelft-CNS-ATM/bluesky-gym)
+
+  A collection of Gymnasium environments for air traffic management tasks, allowing for both civil and urban aviation applications. Build on the open-source air traffic simulator [BlueSky](https://github.com/TUDelft-CNS-ATM/bluesky).
+
 - [gym-electric-motor: Gym environments for electric motor simulations](https://github.com/upb-lea/gym-electric-motor)
 
   An environment for simulating a wide variety of electric drives taking into account different types of electric motors and converters.

docs/tutorials/README.rst

@@ -1,2 +1,7 @@
 Tutorials
 =========
+In this section, we cover some of the most well-known benchmarks of RL including the Frozen Lake, Black Jack, and Training using REINFORCE for Mujoco.
+
+Additionally, we provide a guide on how to load custom quadruped robot environments, implementing custom wrappers, creating custom environments, handling time limits, and training A2C with Vector Envs and Domain Randomization.
+
+Lastly, there is a guide on third-party integrations with Gymnasium.

docs/tutorials/gymnasium_basics/README.rst

@@ -1,5 +1,14 @@
-Gymnasium Basics
+Gymnasium Basics Documentation Links
 ----------------
+Load custom quadruped robot environments link: https://gymnasium.farama.org/tutorials/gymnasium_basics/load_quadruped_model/
+
+Implementing Custom Wrappers link: https://gymnasium.farama.org/tutorials/gymnasium_basics/implementing_custom_wrappers/
+
+Make your own custom environment(environment_creation.py): https://gymnasium.farama.org/tutorials/gymnasium_basics/environment_creation/
+
+Handling Time Limits: https://gymnasium.farama.org/tutorials/gymnasium_basics/handling_time_limits/
+
+Training A2C with Vector Envs and Domain Randomization: https://gymnasium.farama.org/tutorials/gymnasium_basics/vector_envs_tutorial/
 
 .. toctree::
    :hidden:

docs/tutorials/third-party-tutorials.md

@@ -9,6 +9,15 @@
 
 ## Third-Party tutorials about gymnasium
 
+### [Johnny Code from Youtube](https://www.youtube.com/playlist?list=PL58zEckBH8fCt_lYkmayZoR9XfDCW9hte)
+
+Watch this set of videos to train Gymnasium Reinforcement Learning environments using Q-Learning, Deep Q-Learning, and other algorithms. Environments include FrozenLake-v1, MountainCar-v0, CartPole-v1, and MoJoco Humanoid-v4. The youtuber uses Python with VSCode as the development environment. Libraries include Farama Gymnasium, PyTorch, Stable Baselines3. All codes are made available on github.com/johnnycode8 repository.
+
+### [freeCodeCamp from Youtube](https://www.youtube.com/watch?v=vufTSJbzKGU)
+
+This video tells the basics of reinforcement learning and how to implement it using Gymnasium.
+
+
 ## Third-Party tutorials about libraries that use gymnasium
 
 ### [AgileRL](https://docs.agilerl.com/en/latest/tutorials/gymnasium/index.html)

docs/tutorials/training_agents/README.rst

@@ -1,2 +1,7 @@
-Training Agents
----------------
+Training Agents links in the Gymnasium Documentation
+-----------------------------------------------------
+Solving Blackjack with Q-Learning link: https://gymnasium.farama.org/tutorials/training_agents/blackjack_tutorial/
+
+Frozen Lake Benchmark link: https://gymnasium.farama.org/tutorials/training_agents/FrozenLake_tuto/
+
+Training using REINFORCE for Mujoco link: https://gymnasium.farama.org/tutorials/training_agents/reinforce_invpend_gym_v26/