# Gymnasium-Planar-Robotics (GymPR)
This library contains reinforcement learning environments for motion planning and object manipulation in the field of planar robotics. The environments follow either the [Gymnasium](https://gymnasium.farama.org/) API for single-agent RL or the [PettingZoo parallel API](https://pettingzoo.farama.org/api/parallel/) for multi-agent RL. All environments are based on the [MuJoCo](https://mujoco.org/) physics engine. Note that this library depends on the latest MuJoCo Python bindings.
[mujoco-py](https://github.com/openai/mujoco-py) is not supported.
<img src="https://github.com/ubi-coro/gymnasium-planar-robotics/raw/main/docs/images/visual_abstract.png" />
## Installation
The Gymnasium-Planar-Robotics package can be installed via PIP:
```
pip install gymnasium-planar-robotics
```
To install optional dependencies, to build the documentation, or to run the tests, use:
```
pip install gymnasium-planar-robotics[docs, tests]
```
**Note:** Depending on your shell (e.g. when using Zsh), you may need to use additional quotation marks:
```
pip install "gymnasium-planar-robotics[docs, tests]"
```
## Documentation
The documentation is available at: [https://ubi-coro.github.io/gymnasium-planar-robotics/](https://ubi-coro.github.io/gymnasium-planar-robotics/)
## License
GymPR is published under the GNU General Public License v3.0.
## Example
The following example shows how to use a trained policy with an example environment that follows the Gymnasium API:
```python
import gymnasium as gym
env = gym.make("BenchmarkPushingEnv-v0", render_mode="human")
observation, info = env.reset(seed=42)
for _ in range(0,100):
while not terminated and not truncated:
action = policy(observation) # custom policy
observation, reward, terminated, truncated, info = env.step(action)
observation, info = env.reset()
env.close()
```
## Maintainer
Gymnasium-Planar-Robotics is currently maintained by Lara Bergmann (@lbergmann1).
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"description": "# Gymnasium-Planar-Robotics (GymPR)\nThis library contains reinforcement learning environments for motion planning and object manipulation in the field of planar robotics. The environments follow either the [Gymnasium](https://gymnasium.farama.org/) API for single-agent RL or the [PettingZoo parallel API](https://pettingzoo.farama.org/api/parallel/) for multi-agent RL. All environments are based on the [MuJoCo](https://mujoco.org/) physics engine. Note that this library depends on the latest MuJoCo Python bindings. \n[mujoco-py](https://github.com/openai/mujoco-py) is not supported.\n\n<img src=\"https://github.com/ubi-coro/gymnasium-planar-robotics/raw/main/docs/images/visual_abstract.png\" />\n\n## Installation\nThe Gymnasium-Planar-Robotics package can be installed via PIP:\n```\npip install gymnasium-planar-robotics\n```\nTo install optional dependencies, to build the documentation, or to run the tests, use:\n```\npip install gymnasium-planar-robotics[docs, tests]\n```\n**Note:** Depending on your shell (e.g. when using Zsh), you may need to use additional quotation marks: \n```\npip install \"gymnasium-planar-robotics[docs, tests]\"\n```\n\n## Documentation\nThe documentation is available at: [https://ubi-coro.github.io/gymnasium-planar-robotics/](https://ubi-coro.github.io/gymnasium-planar-robotics/)\n\n## License\nGymPR is published under the GNU General Public License v3.0.\n\n## Example\nThe following example shows how to use a trained policy with an example environment that follows the Gymnasium API:\n\n```python\nimport gymnasium as gym\n\nenv = gym.make(\"BenchmarkPushingEnv-v0\", render_mode=\"human\")\nobservation, info = env.reset(seed=42)\n\nfor _ in range(0,100):\n while not terminated and not truncated:\n action = policy(observation) # custom policy\n observation, reward, terminated, truncated, info = env.step(action)\n\n observation, info = env.reset()\nenv.close()\n```\n\n## Maintainer\nGymnasium-Planar-Robotics is currently maintained by Lara Bergmann (@lbergmann1).\n",
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