xenoverse 0.1.10.0

Collection of xeno-world environments for meta-training of general-purpose learning agents (GLAs)

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: Procedurally generated unlimited general-purpose Markov Decision Processes (MDP) in discrete spaces.

• AnyHVAC: Procedurally generated random room and equipments for Heating, Ventilation, and Air Conditioning (HVAC) control

• MetaLanguage: Pseudo-language generated from randomized neural networks, benchmarking in-context language learning (ICLL).

• MazeWorld: Procedurally generated immersed 3D mazes with diverse maze structures.

• MetaControl: Randomized environments for classic control and locomotions.

Installation

pip install xenoverse

Reference

Related works

@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}
}