Metadata-Version: 2.3
Name: scaling-vin-pytorch
Version: 0.0.10
Summary: Scaling Value Iteration Networks
Project-URL: Homepage, https://pypi.org/project/scaling-vin-pytorch/
Project-URL: Repository, https://github.com/lucidrains/scaling-vin-pytorch
Author-email: Phil Wang <lucidrains@gmail.com>
License: MIT License
        
        Copyright (c) 2024 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
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License-File: LICENSE
Keywords: artificial intelligence,deep learning,planning,value iteration network
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: beartype
Requires-Dist: einops>=0.8.0
Requires-Dist: einx>=0.3.0
Requires-Dist: torch>=2.0
Requires-Dist: x-transformers>=1.35.3
Provides-Extra: examples
Provides-Extra: test
Requires-Dist: pytest; extra == 'test'
Description-Content-Type: text/markdown

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

## Scaling Value Iteration Networks (wip)

Exploration into the <a href="https://arxiv.org/abs/2406.08404">Scaling Value Iteration Networks</a> paper, from Schmidhuber's group

## Usage

```python
import torch
from scaling_vin_pytorch import ScalableVIN

scalable_vin = ScalableVIN(
    state_dim = 3,
    reward_dim = 2,
    num_actions = 10
)

state = torch.randn(2, 3, 32, 32)
reward = torch.randn(2, 2, 32, 32)

agent_positions = torch.randint(0, 32, (2, 2))

target_actions = torch.randint(0, 10, (2,))

loss = scalable_vin(
    state,
    reward,
    agent_positions,
    target_actions
)

loss.backward()

action_logits = scalable_vin(
    state,
    reward,
    agent_positions
)
```

## Citations

```bibtex
@article{Wang2024ScalingVI,
    title   = {Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning},
    author  = {Yuhui Wang and Qingyuan Wu and Weida Li and Dylan R. Ashley and Francesco Faccio and Chao Huang and J{\"u}rgen Schmidhuber},
    journal = {ArXiv},
    year    = {2024},
    volume  = {abs/2406.08404},
    url     = {https://api.semanticscholar.org/CorpusID:270391752}
}
```

```bibtex
@misc{pflueger2018soft,
    title   = {Soft Value Iteration Networks for Planetary Rover Path Planning},
    author  = {Max Pflueger and Ali Agha and Gaurav S. Sukhatme},
    year    = {2018},
    url     = {https://openreview.net/forum?id=Sktm4zWRb},
}
```

```bibtex
@inproceedings{Tamar2016ValueIN,
    title   = {Value Iteration Networks},
    author  = {Aviv Tamar and Sergey Levine and P. Abbeel and Yi Wu and Garrett Thomas},
    booktitle = {Neural Information Processing Systems},
    year    = {2016},
    url     = {https://api.semanticscholar.org/CorpusID:11374605}
}
```
