# panda-gym
Set of robotic environments based on PyBullet physics engine and gymnasium.
[](https://pypi.org/project/panda-gym/)
[](https://pepy.tech/project/panda-gym)
[](LICENSE.txt)
[](https://github.com/qgallouedec/panda-gym/actions/workflows/build.yml)
[](https://codecov.io/gh/qgallouedec/panda-gym)
[](https://github.com/psf/black)
[](https://arxiv.org/abs/2106.13687)
## Documentation
Check out the [documentation](https://panda-gym.readthedocs.io/en/latest/).
## Installation
### Using PyPI
```bash
pip install panda-gym
```
### From source
```bash
git clone https://github.com/qgallouedec/panda-gym.git
pip install -e panda-gym
```
## Usage
```python
import gymnasium as gym
import panda_gym
env = gym.make('PandaReach-v3', render_mode="human")
observation, info = env.reset()
for _ in range(1000):
action = env.action_space.sample() # random action
observation, reward, terminated, truncated, info = env.step(action)
if terminated or truncated:
observation, info = env.reset()
env.close()
```
You can also [](https://colab.research.google.com/github/qgallouedec/panda-gym/blob/master/examples/PickAndPlace.ipynb)
## Environments
| | |
| :------------------------------: | :--------------------------------------------: |
| `PandaReach-v3` | `PandaPush-v3` |
|  |  |
| `PandaSlide-v3` | `PandaPickAndPlace-v3` |
|  |  |
| `PandaStack-v3` | `PandaFlip-v3` |
|  |  |
## Baselines results
Baselines results are available in [rl-baselines3-zoo](https://github.com/DLR-RM/rl-baselines3-zoo) and the pre-trained agents in the [Hugging Face Hub](https://huggingface.co/sb3).
## Citation
Cite as
```bib
@article{gallouedec2021pandagym,
title = {{panda-gym: Open-Source Goal-Conditioned Environments for Robotic Learning}},
author = {Gallou{\'e}dec, Quentin and Cazin, Nicolas and Dellandr{\'e}a, Emmanuel and Chen, Liming},
year = 2021,
journal = {4th Robot Learning Workshop: Self-Supervised and Lifelong Learning at NeurIPS},
}
```
Environments are widely inspired from [OpenAI Fetch environments](https://openai.com/blog/ingredients-for-robotics-research/).
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"description": "# panda-gym\n\nSet of robotic environments based on PyBullet physics engine and gymnasium.\n\n[](https://pypi.org/project/panda-gym/)\n[](https://pepy.tech/project/panda-gym)\n[](LICENSE.txt)\n[](https://github.com/qgallouedec/panda-gym/actions/workflows/build.yml)\n[](https://codecov.io/gh/qgallouedec/panda-gym)\n[](https://github.com/psf/black)\n[](https://arxiv.org/abs/2106.13687)\n\n## Documentation\n\nCheck out the [documentation](https://panda-gym.readthedocs.io/en/latest/).\n\n## Installation\n\n### Using PyPI\n\n```bash\npip install panda-gym\n```\n\n### From source\n\n```bash\ngit clone https://github.com/qgallouedec/panda-gym.git\npip install -e panda-gym\n```\n\n## Usage\n\n```python\nimport gymnasium as gym\nimport panda_gym\n\nenv = gym.make('PandaReach-v3', render_mode=\"human\")\n\nobservation, info = env.reset()\n\nfor _ in range(1000):\n action = env.action_space.sample() # random action\n observation, reward, terminated, truncated, info = env.step(action)\n\n if terminated or truncated:\n observation, info = env.reset()\n\nenv.close()\n```\n\nYou can also [](https://colab.research.google.com/github/qgallouedec/panda-gym/blob/master/examples/PickAndPlace.ipynb)\n\n## Environments\n\n| | |\n| :------------------------------: | :--------------------------------------------: |\n| `PandaReach-v3` | `PandaPush-v3` |\n|  |  |\n| `PandaSlide-v3` | `PandaPickAndPlace-v3` |\n|  |  |\n| `PandaStack-v3` | `PandaFlip-v3` |\n|  |  |\n\n## Baselines results\n\nBaselines results are available in [rl-baselines3-zoo](https://github.com/DLR-RM/rl-baselines3-zoo) and the pre-trained agents in the [Hugging Face Hub](https://huggingface.co/sb3).\n\n## Citation\n\nCite as\n\n```bib\n@article{gallouedec2021pandagym,\n title = {{panda-gym: Open-Source Goal-Conditioned Environments for Robotic Learning}},\n author = {Gallou{\\'e}dec, Quentin and Cazin, Nicolas and Dellandr{\\'e}a, Emmanuel and Chen, Liming},\n year = 2021,\n journal = {4th Robot Learning Workshop: Self-Supervised and Lifelong Learning at NeurIPS},\n}\n```\n\nEnvironments are widely inspired from [OpenAI Fetch environments](https://openai.com/blog/ingredients-for-robotics-research/). \n",
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