Fancy Gym
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.
**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.
- **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.
- **Benchmark Suite Compatibility**: `fancy_gym` makes it easy to access renowned benchmark suites such as [DeepMind Control](dominik-roth.eu/fancy/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.
- **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`.
## Quickstart Guide
| ℹ 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 `\_\_)
```bash
pip install 'fancy_gym[all]'
```
Try out one of our step-based environments (`or explore our other envs `\_\_)
```python
import gymnasium as gym
import fancy_gym
import time
env = gym.make('fancy/BoxPushingDense-v0', render_mode='human')
observation = env.reset()
env.render()
for i in range(1000):
action = env.action_space.sample() # Randomly sample an action
observation, reward, terminated, truncated, info = env.step(action)
time.sleep(1/env.metadata['render_fps'])
if terminated or truncated:
observation, info = env.reset()
```
Explore the MP-based variant (`or learn more about Movement Primitives (MPs) `\_\_)
```python
import gymnasium as gym
import fancy_gym
env = gym.make('fancy_ProMP/BoxPushingDense-v0', render_mode='human')
env.reset()
env.render()
for i in range(10):
action = env.action_space.sample() # Randomly sample MP parameters
observation, reward, terminated, truncated, info = env.step(action) # Will execute full trajectory, based on MP
observation = env.reset()
```
## 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).
## Citing the Project
To cite this repository in publications:
```bibtex
@software{fancy_gym,
title = {Fancy Gym},
author = {Otto, Fabian and Celik, Onur and Roth, Dominik and Zhou, Hongyi},
abstract = {Fancy Gym: Unifying interface for various RL benchmarks with support for Black Box approaches.},
url = {https://github.com/ALRhub/fancy_gym},
organization = {Autonomous Learning Robots Lab (ALR) at KIT},
}
```
## Icon Attribution
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).