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Tutorials
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=========
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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.
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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.
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Lastly, there is a guide on third-party integrations with Gymnasium.
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Gymnasium Basics
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Gymnasium Basics Documentation Links
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----------------
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Load custom quadruped robot environments link: https://gymnasium.farama.org/tutorials/gymnasium_basics/load_quadruped_model/
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Implementing Custom Wrappers link: https://gymnasium.farama.org/tutorials/gymnasium_basics/implementing_custom_wrappers/
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Make your own custom environment(environment_creation.py): https://gymnasium.farama.org/tutorials/gymnasium_basics/environment_creation/
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Handling Time Limits: https://gymnasium.farama.org/tutorials/gymnasium_basics/handling_time_limits/
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Training A2C with Vector Envs and Domain Randomization: https://gymnasium.farama.org/tutorials/gymnasium_basics/vector_envs_tutorial/
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.. toctree::
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:hidden:
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Training Agents
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---------------
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Training Agents links in the Gymnasium Documentation
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-----------------------------------------------------
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Solving Blackjack with Q-Learning link: https://gymnasium.farama.org/tutorials/training_agents/blackjack_tutorial/
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Frozen Lake Benchmark link: https://gymnasium.farama.org/tutorials/training_agents/FrozenLake_tuto/
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Training using REINFORCE for Mujoco link: https://gymnasium.farama.org/tutorials/training_agents/reinforce_invpend_gym_v26/
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