Blocksworld3D, building upon the MiniWorld environment, specifically targets the RoomObjects scenario. Designed for reinforcement learning and robotics research, this minimalistic environment aligns with the MiniWorld API.
Blocksworld3D extends the traditional BlocksWorld planning problem to a 3D domain, adding extra rows of blocks. It presents eight problems, each with unique constraints on initial block positioning and final objectives.
Install from PyPi using:
pip install blocksworld3d
Or install from the source using:
git clone https://github.com/ethanmclark1/blocksworld3d.git
cd blocksworld3d
pip install -r requirements.txt
pip install -e .
import blocksworld3d
env = blocksworld3d.env()
env.reset(options={'problem_instance': 'gap'}))
observation, _, terminations, truncations, _ = env.last()
env.step(action)
env.close()
| Problem Instance |
|---|
gap |
balance |
stairs |
pyramid |
bed |
towers |
wave-v0 |
wave-v1 |
We welcome contributions to Blocksworld3d! Whether it's bug reports, feature requests, or pull requests, your collaboration helps make Blocksworld3d better.
If you have questions or need support, please contact us at eclark715@gmail.com, or create an issue in the GitHub repository.
Blocksworld3d is open-source software licensed under the MIT license.
If you found this environment helpful, consider citing relevant papers. Here's an example citation for the original MiniWorld environment:
@article{MinigridMiniworld23,
author={Maxime Chevalier-Boisvert and Bolun Dai and Mark Towers and Rodrigo de Lazcano and Lucas Willems and Salem Lahlou and Suman Pal and Pablo Samuel Castro and Jordan Terry},
title={Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks},
journal={CoRR},
volume={abs/2306.13831}
year={2023}
}