Metadata-Version: 2.5
Name: survival-rl
Version: 0.0.1
Summary: Explorations into Survival Reinforcement Learning
Project-URL: Homepage, https://pypi.org/project/survival-rl/
Project-URL: Repository, https://github.com/lucidrains/survival-rl
Author-email: Phil Wang <lucidrains@gmail.com>
License: MIT License
        
        Copyright (c) 2026 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,reinforcement learning,survival analysis
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Requires-Dist: einops>=0.8.2
Requires-Dist: einx>=0.4.3
Requires-Dist: torch-einops-utils>=0.1.31
Requires-Dist: torch>=2.5
Requires-Dist: x-mlps-pytorch>=0.6.4
Provides-Extra: examples
Provides-Extra: test
Requires-Dist: pytest; extra == 'test'
Description-Content-Type: text/markdown

## Survival RL (wip)

Explorations into an application of survival analysis to RL, proposed by Tiofack et al. earlier this year

## Citations

```bibtex
@inproceedings{Tiofack2026SVL,
    title     = {SVL: Goal-Conditioned Reinforcement Learning as Survival Learning},
    author    = {Franki Nguimatsia Tiofack and Fabian Schramm and Th{\'e}otime Le Hellard and Justin Carpentier},
    booktitle = {International Conference on Machine Learning (ICML)},
    year      = {2026},
    url       = {https://arxiv.org/abs/2604.17551}
}
```

```bibtex
@article{Tiofack2026SRL,
    title   = {Survival Reinforcement Learning: Toward Scalable Self-Supervised RL},
    author  = {Franki Nguimatsia-Tiofack and Fabian Schramm and Th{\'e}otime Le Hellard and Justin Carpentier},
    journal = {arXiv preprint arXiv:2605.31273},
    year    = {2026},
    note    = {19th European Workshop on Reinforcement Learning (EWRL), Oral},
    url     = {https://arxiv.org/abs/2605.31273}
}
```
