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.
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/
Training A2C with Vector Envs and Domain Randomization
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/
