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# Fancy Gym # Fancy Gym
`fancy_gym` offers a large variety of reinforcement learning environments under the unifying interface `fancy_gym` offers a large variety of reinforcement learning environments under the unifying interface
of [OpenAI gym](https://gym.openai.com/). We provide support (under the OpenAI gym interface) for the benchmark suites of [OpenAI gym](https://gymlibrary.dev/). We provide support (under the OpenAI gym interface) for the benchmark suites
[DeepMind Control](https://deepmind.com/research/publications/2020/dm-control-Software-and-Tasks-for-Continuous-Control) [DeepMind Control](https://deepmind.com/research/publications/2020/dm-control-Software-and-Tasks-for-Continuous-Control)
(DMC) and [Metaworld](https://meta-world.github.io/). If those are not sufficient and you want to create your own custom (DMC) and [Metaworld](https://meta-world.github.io/). If those are not sufficient and you want to create your own custom
gym environments, use [this guide](https://www.gymlibrary.ml/content/environment_creation/). We highly appreciate it, if gym environments, use [this guide](https://www.gymlibrary.dev/content/environment_creation/). We highly appreciate it, if
you would then submit a PR for this environment to become part of `fancy_gym`. you would then submit a PR for this environment to become part of `fancy_gym`.
In comparison to existing libraries, we additionally support to control agents with movement primitives, such as Dynamic In comparison to existing libraries, we additionally support to control agents with movement primitives, such as Dynamic
Movement Primitives (DMPs) and Probabilistic Movement Primitives (ProMP). Movement Primitives (DMPs) and Probabilistic Movement Primitives (ProMP).