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Update third_party_environments.md first-party environments section (#1026)
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:tocdepth: 2
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```
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# Third-Party Environments
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## First-Party Environments
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The Farama Foundation maintains a number of other [projects](https://farama.org/projects), which use the Gymnasium API, environments include:
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The Farama Foundation maintains a number of other [projects](https://farama.org/projects), most of which use Gymnasium. Topics include:
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multi-agent RL ([PettingZoo](https://pettingzoo.farama.org/)),
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offline-RL ([Minari](https://minari.farama.org/)),
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gridworlds ([Minigrid](https://minigrid.farama.org/)),
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robotics ([Gymnasium-Robotics](https://robotics.farama.org/)),
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3D navigation ([Miniworld](https://miniworld.farama.org/)),
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web interaction ([MiniWoB++](https://miniwob.farama.org/))
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arcade games ([Arcade Learning Environment](https://github.com/Farama-Foundation/Arcade-Learning-Environment))
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Doom ([ViZDoom](https://vizdoom.farama.org/))
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Meta-objective robotics ([Metaworld](https://metaworld.farama.org/))
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autonomous driving ([HighwayEnv](https://highway-env.farama.org/))
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Retro Games ([stable-retro](https://github.com/Farama-Foundation/stable-retro)), and many more.
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The Farama Foundation also maintains alternate APIs for RL, including:
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multi-agent RL ([PettingZoo](https://pettingzoo.farama.org/)),
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offline-RL ([Minari](https://minari.farama.org/)),
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multi-objective RL ([MO-Gymnasium](https://mo-gymnasium.farama.org/))
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many-agent RL ([MAgent2](https://magent2.farama.org/)),
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3D navigation ([Miniworld](https://miniworld.farama.org/)), and many more.
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goal-RL ([Gymnasium-Robotics](https://robotics.farama.org/)),
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## Third-party environments with Gymnasium
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@@ -82,13 +92,6 @@ An environment for guiding automated theorem provers based on saturation algorit
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Gym Trading Env simulates stock (or crypto) market from historical data. It was designed to be fast and easily customizable.
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### [highway-env: Autonomous driving and tactical decision-making tasks](https://github.com/eleurent/highway-env)
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An environment for behavioral planning in autonomous driving, with an emphasis on high-level perception and decision rather than low-level sensing and control.
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### [matrix-mdp: Easily create discrete MDPs](https://github.com/Paul-543NA/matrix-mdp-gym)
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@@ -145,13 +148,6 @@ spark-sched-sim simulates Spark clusters for RL-based job scheduling algorithms.
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### [stable-retro: Classic retro games, a maintained version of OpenAI Retro](https://github.com/Farama-Foundation/stable-retro)
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Supported fork of [gym-retro](https://openai.com/research/gym-retro): turn classic video games into Gymnasium environments.
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### [sumo-rl: Reinforcement Learning using SUMO traffic simulator](https://github.com/LucasAlegre/sumo-rl)
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