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

Project description

L3C

L3C provides variant environments facilitating In-Context Learning, featured in the following assets:

  • Vast amounts of diversified tasks with minimal inductive bias
  • Lifelong In-Context Learning
  • Generative and Interactive

Environments Updating

  • AnyMDP: Procedurally generated unlimited general-purpose Markov Decision Processes (MDP)

  • MetaLanguage: Pseudo-language generated from randomized neural networks

  • MazeWorld: Procedurally generated mazes with diverse maze structures, navigation goals to benchmark object navigation

  • L3C_Baselines: An implementation of the baseline solutions to the above tasks

Installation

pip install l3c

Reference

Cite this work with

@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}
}

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