88 lines
4.4 KiB
Markdown
88 lines
4.4 KiB
Markdown
<h1 align="center">
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<br>
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<img src='https://raw.githubusercontent.com/ALRhub/fancy_gym/master/icon.svg' width="250px">
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<br><br>
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<b>Fancy Gym</b>
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<br><br>
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</h1>
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Built upon the foundation of [Gymnasium](https://gymnasium.farama.org) (a maintained fork of OpenAI’s renowned Gym library) `fancy_gym` offers a comprehensive collection of reinforcement learning environments.
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**Key Features**:
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- **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.
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- **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).
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- **Upgrade to Movement Primitives**: With our framework, it’s straightforward to transform standard Gymnasium environments into environments that support movement primitives.
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- **Benchmark Suite Compatibility**: `fancy_gym` makes it easy to access renowned benchmark suites such as [DeepMind Control](dominik-roth.eu/fancy/envs/dmc.html)
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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.
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- **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`.
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## Quickstart Guide
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| ⚠ 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/). |
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| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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Install via pip [or use an alternative installation method](https://dominik-roth.eu/fancy/guide/installation.html)
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```bash
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pip install 'fancy_gym[all]'
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```
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Try out one of our step-based environments [or explore our other envs](https://dominik-roth.eu/fancy/envs/fancy/index.html)
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```python
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import gymnasium as gym
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import fancy_gym
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import time
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env = gym.make('fancy/BoxPushingDense-v0', render_mode='human')
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observation = env.reset()
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env.render()
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for i in range(1000):
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action = env.action_space.sample() # Randomly sample an action
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observation, reward, terminated, truncated, info = env.step(action)
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time.sleep(1/env.metadata['render_fps'])
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if terminated or truncated:
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observation, info = env.reset()
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```
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Explore the MP-based variant [or learn more about Movement Primitives (MPs)](https://dominik-roth.eu/fancy/guide/episodic_rl.html)
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```python
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import gymnasium as gym
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import fancy_gym
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env = gym.make('fancy_ProMP/BoxPushingDense-v0', render_mode='human')
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env.reset()
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env.render()
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for i in range(10):
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action = env.action_space.sample() # Randomly sample MP parameters
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observation, reward, terminated, truncated, info = env.step(action) # Will execute full trajectory, based on MP
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observation = env.reset()
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```
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## Documentation
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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).
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## Citing the Project
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To cite this repository in publications:
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```bibtex
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@software{fancy_gym,
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title = {Fancy Gym},
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author = {Otto, Fabian and Celik, Onur and Roth, Dominik and Zhou, Hongyi},
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abstract = {Fancy Gym: Unifying interface for various RL benchmarks with support for Black Box approaches.},
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url = {https://github.com/ALRhub/fancy_gym},
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organization = {Autonomous Learning Robots Lab (ALR) at KIT},
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}
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```
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## Icon Attribution
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The icon is based on the [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) icon as can be found [here](https://gymnasium.farama.org/_static/img/gymnasium_black.svg).
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