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@ -10,25 +10,25 @@ Built upon the foundation of [Gymnasium](https://gymnasium.farama.org) (a mainta
**Key Features**: **Key Features**:
- **New Challenging Environments**: `fancy_gym` includes several new environments ([Panda Box Pushing](https://dominik-roth.eu/fancy/envs/fancy/mujoco.html#box-pushing), [Table Tennis](https://dominik-roth.eu/fancy/envs/fancy/mujoco.html#table-tennis), [etc.](https://dominik-roth.eu/fancy/envs/fancy/index.html)) that present a higher degree of difficulty, pushing the boundaries of reinforcement learning research. - **New Challenging Environments**: `fancy_gym` includes several new environments ([Panda Box Pushing](https://alrhub.github.io/fancy_gym/envs/fancy/mujoco.html#box-pushing), [Table Tennis](https://alrhub.github.io/fancy_gym/envs/fancy/mujoco.html#table-tennis), [etc.](https://alrhub.github.io/fancy_gym/envs/fancy/index.html)) that present a higher degree of difficulty, pushing the boundaries of reinforcement learning research.
- **Support for Movement Primitives**: `fancy_gym` supports a range of movement primitives (MPs), including Dynamic Movement Primitives (DMPs), Probabilistic Movement Primitives (ProMP), and Probabilistic Dynamic Movement Primitives (ProDMP). - **Support for Movement Primitives**: `fancy_gym` supports a range of movement primitives (MPs), including Dynamic Movement Primitives (DMPs), Probabilistic Movement Primitives (ProMP), and Probabilistic Dynamic Movement Primitives (ProDMP).
- **Upgrade to Movement Primitives**: With our framework, it’s straightforward to transform standard Gymnasium environments into environments that support movement primitives. - **Upgrade to Movement Primitives**: With our framework, it’s straightforward to transform standard Gymnasium environments into environments that support movement primitives.
- **Benchmark Suite Compatibility**: `fancy_gym` makes it easy to access renowned benchmark suites such as [DeepMind Control](dominik-roth.eu/fancy/envs/dmc.html) - **Benchmark Suite Compatibility**: `fancy_gym` makes it easy to access renowned benchmark suites such as [DeepMind Control](https://alrhub.github.io/fancy_gym/envs/dmc.html)
and [Metaworld](https://dominik-roth.eu/fancy/envs/meta.html), whether you want to use them in the regular step-based setting or using MPs. and [Metaworld](https://alrhub.github.io/fancy_gym/envs/meta.html), whether you want to use them in the regular step-based setting or using MPs.
- **Contribute Your Own Environments**: If you’re inspired to create custom gym environments, both step-based and with movement primitives, this [guide](https://dominik-roth.eu/fancy/guide/upgrading_envs.html) will assist you. We encourage and highly appreciate submissions via PRs to integrate these environments into `fancy_gym`. - **Contribute Your Own Environments**: If you’re inspired to create custom gym environments, both step-based and with movement primitives, this [guide](https://alrhub.github.io/fancy_gym/guide/upgrading_envs.html) will assist you. We encourage and highly appreciate submissions via PRs to integrate these environments into `fancy_gym`.
## Quickstart Guide ## Quickstart Guide
| ⚠ We recommend installing `fancy_gym` into a virtual environment as provided by [venv](https://docs.python.org/3/library/venv.html), [Poetry](https://python-poetry.org/) or [Conda](https://docs.conda.io/en/latest/). | | ⚠ We recommend installing `fancy_gym` into a virtual environment as provided by [venv](https://docs.python.org/3/library/venv.html), [Poetry](https://python-poetry.org/) or [Conda](https://docs.conda.io/en/latest/). |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
Install via pip [or use an alternative installation method](https://dominik-roth.eu/fancy/guide/installation.html) Install via pip [or use an alternative installation method](https://alrhub.github.io/fancy_gym/guide/installation.html)
```bash ```bash
pip install 'fancy_gym[all]' pip install 'fancy_gym[all]'
``` ```
Try out one of our step-based environments [or explore our other envs](https://dominik-roth.eu/fancy/envs/fancy/index.html) Try out one of our step-based environments [or explore our other envs](https://alrhub.github.io/fancy_gym/envs/fancy/index.html)
```python ```python
import gymnasium as gym import gymnasium as gym
@ -48,7 +48,7 @@ Try out one of our step-based environments [or explore our other envs](https://d
observation, info = env.reset() observation, info = env.reset()
``` ```
Explore the MP-based variant [or learn more about Movement Primitives (MPs)](https://dominik-roth.eu/fancy/guide/episodic_rl.html) Explore the MP-based variant [or learn more about Movement Primitives (MPs)](https://alrhub.github.io/fancy_gym/guide/episodic_rl.html)
```python ```python
import gymnasium as gym import gymnasium as gym
@ -66,7 +66,7 @@ Explore the MP-based variant [or learn more about Movement Primitives (MPs)](htt
## Documentation ## Documentation
Documentation for `fancy_gym` can be found [here](https://dominik-roth.eu/fancy); Usage Examples can be found [here](https://dominik-roth.eu/fancy/examples/general.html). Documentation for `fancy_gym` can be found [here](https://alrhub.github.io/fancy_gym/); Usage Examples can be found [here](https://alrhub.github.io/fancy_gym/examples/general.html).
## Citing the Project ## Citing the Project