semvec/pypi-packages huggingface dataset:
xenoverse 0.1.10.0
Collection of xeno-world environments for meta-training of general-purpose learning agents (GLAs)
License: Apache
Requires Python: >=3.8

# Xenoverse: Toward Training General-Purpose Learning Agents (GLA) with Randomized Worlds

## xenoverse instead of a single universe

The recent research indicates that the generalization ability of learning agents is primarily dependent on the diversity of training environments. However, the real-world poses a significant limitation on the diversity itself, e.g., physical laws, the gravitational constant is almost constant. We believe this limitation is serious bottleneck to incentivize artificial general intelligence (AGI).

Xenoverse is a collection of extremely diverse worlds by procedural generation based on completely random parameters. We propose that AGI should not be trained and adapted in a single universe, but in xenoverse.

## collection of xenoverse environments

- [AnyMDP](xenoverse/anymdp): Procedurally generated unlimited general-purpose Markov Decision Processes (MDP) in discrete spaces.

- [AnyHVAC](xenoverse/anyhvac): Procedurally generated random room and equipments for Heating, Ventilation, and Air Conditioning (HVAC) control

- [MetaLanguage](xenoverse/metalang): Pseudo-language generated from randomized neural networks, benchmarking in-context language learning (ICLL).

- [MazeWorld](xenoverse/mazeworld): Procedurally generated immersed 3D mazes with diverse maze structures.

- [MetaControl](xenoverse/metacontrol): Randomized environments for classic control and locomotions.


# Installation

```bash
pip install xenoverse
```

# Reference
Related works
```bibtex
@article{wang2024benchmarking,
  title={Benchmarking General Purpose In-Context Learning},
  author={Wang, Fan and Lin, Chuan and Cao, Yang and Kang, Yu},
  journal={arXiv preprint arXiv:2405.17234},
  year={2024}
}
@inproceedings{
  wang2025towards,
  title={Towards Large-Scale In-Context Reinforcement Learning by Meta-Training in Randomized Worlds},
  author={Fan Wang and Pengtao Shao and Yiming Zhang and Bo Yu and Shaoshan Liu and Ning Ding and Yang Cao and Yu Kang and Haifeng Wang},
  booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
  year={2025},
  url={https://openreview.net/forum?id=b6ASJBXtgP}
}
@article{fan2025putting,
  title={Putting the smarts into robot bodies},
  author={Fan, Wang and Liu, Shaoshan},
  journal={Communications of the ACM},
  volume={68},
  number={3},
  pages={6--8},
  year={2025},
  publisher={ACM New York, NY, USA}
}
```