Metadata-Version: 2.4
Name: metacontroller-pytorch
Version: 0.0.2
Summary: Transformer Metacontroller
Project-URL: Homepage, https://pypi.org/project/metacontroller/
Project-URL: Repository, https://github.com/lucidrains/metacontroller
Author-email: Phil Wang <lucidrains@gmail.com>
License: MIT License
        
        Copyright (c) 2025 Phil Wang
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
License-File: LICENSE
Keywords: artificial intelligence,deep learning,hierarchical reinforcement learning,latent steering
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.9
Requires-Dist: assoc-scan
Requires-Dist: discrete-continuous-embed-readout>=0.1.11
Requires-Dist: einops>=0.8.1
Requires-Dist: einx>=0.3.0
Requires-Dist: torch>=2.5
Requires-Dist: x-evolution>=0.1.23
Requires-Dist: x-mlps-pytorch
Requires-Dist: x-transformers
Provides-Extra: examples
Provides-Extra: test
Requires-Dist: pytest; extra == 'test'
Description-Content-Type: text/markdown

<img src="./fig1.png" width="400px"></img>

## metacontroller (wip)

Implementation of the MetaController proposed in [Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning](https://arxiv.org/abs/2512.20605)

## Citations

```bibtex
@misc{kobayashi2025emergenttemporalabstractionsautoregressive,
    title   = {Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning}, 
    author  = {Seijin Kobayashi and Yanick Schimpf and Maximilian Schlegel and Angelika Steger and Maciej Wolczyk and Johannes von Oswald and Nino Scherrer and Kaitlin Maile and Guillaume Lajoie and Blake A. Richards and Rif A. Saurous and James Manyika and Blaise Agüera y Arcas and Alexander Meulemans and João Sacramento},
    year={2025},
    eprint  = {2512.20605},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    url     = {https://arxiv.org/abs/2512.20605}, 
}
```

```bibtex
@article{Wagenmaker2025SteeringYD,
    title   = {Steering Your Diffusion Policy with Latent Space Reinforcement Learning},
    author  = {Andrew Wagenmaker and Mitsuhiko Nakamoto and Yunchu Zhang and Seohong Park and Waleed Yagoub and Anusha Nagabandi and Abhishek Gupta and Sergey Levine},
    journal = {ArXiv},
    year    = {2025},
    volume  = {abs/2506.15799},
    url     = {https://api.semanticscholar.org/CorpusID:279464702}
}
```
