Metadata-Version: 2.4
Name: contrastive-rl-pytorch
Version: 0.4.3
Summary: Contrastive RL
Project-URL: Homepage, https://pypi.org/project/contrastive-rl/
Project-URL: Repository, https://github.com/lucidrains/contrastive-rl
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
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        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
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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License-File: LICENSE
Keywords: artificial intelligence,deep learning
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: accelerate
Requires-Dist: discrete-continuous-embed-readout>=0.2.1
Requires-Dist: einops>=0.8.1
Requires-Dist: einx>=0.3.0
Requires-Dist: ema-pytorch>=0.3.0
Requires-Dist: hl-gauss-pytorch>=0.2.2
Requires-Dist: memmap-replay-buffer>=0.0.17
Requires-Dist: torch>=2.4
Requires-Dist: x-mlps-pytorch>=0.3.0
Provides-Extra: examples
Provides-Extra: test
Requires-Dist: pytest; extra == 'test'
Description-Content-Type: text/markdown

<img src="./crtr.png" width="450px"></img>

## contrastive-rl

For following a [new line of research](https://arxiv.org/abs/2206.07568) that started in 2022 from [Eysenbach](https://ben-eysenbach.github.io/) et al.

This is important not because of contrastive learning, but because it happens to be a special case where the RL and SSL algorithm is one. It reveals how "traditional" RL is unable to build up representations alone.

*Update: Finally seeing it, at about 3-5k steps*

## install

```shell
$ pip install contrastive-rl-pytorch
```

## usage

```python
import torch
from contrastive_rl_pytorch import ContrastiveRLTrainer

from x_mlps_pytorch import ResidualNormedMLP # https://arxiv.org/abs/2503.14858

encoder = ResidualNormedMLP(dim = 256, dim_in = 16, dim_out = 128, keel_post_ln = True)

trainer = ContrastiveRLTrainer(encoder)

trajectories = torch.randn(256, 512, 16)

trainer(trajectories, 100)

# train for 100 steps and save

torch.save(encoder.state_dict(), './trained.pt')
```

## quick test

make sure `uv` is installed `pip install uv`

then

```shell
$ uv run train_lunar.py --cpu
```

wait until 3-5k steps at least

## citations

```bibtex
@misc{eysenbach2023contrastivelearninggoalconditionedreinforcement,
    title   = {Contrastive Learning as Goal-Conditioned Reinforcement Learning},
    author  = {Benjamin Eysenbach and Tianjun Zhang and Ruslan Salakhutdinov and Sergey Levine},
    year    = {2023},
    eprint  = {2206.07568},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    url     = {https://arxiv.org/abs/2206.07568},
}
```

```bibtex
@misc{ziarko2025contrastiverepresentationstemporalreasoning,
    title   = {Contrastive Representations for Temporal Reasoning},
    author  = {Alicja Ziarko and Michal Bortkiewicz and Michal Zawalski and Benjamin Eysenbach and Piotr Milos},
    year    = {2025},
    eprint  = {2508.13113},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    url     = {https://arxiv.org/abs/2508.13113},
}
```

```bibtex
@inproceedings{anonymous2025hierarchical,
    title   = {Hierarchical Contrastive Reinforcement Learning: learn representation more suitable for {RL} environments},
    author  = {Anonymous},
    booktitle = {Submitted to The Fourteenth International Conference on Learning Representations},
    year    = {2025},
    url     = {https://openreview.net/forum?id=rTCSFOzVcK},
    note    = {under review}
}
```

```bibtex
@misc{liu2024singlegoalneedskills,
    title   = {A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoals},
    author  = {Grace Liu and Michael Tang and Benjamin Eysenbach},
    year    = {2024},
    eprint  = {2408.05804},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    url     = {https://arxiv.org/abs/2408.05804},
}
```

```bibtex
@inproceedings{anonymous2025demystifying,
    title   = {Demystifying Emergent Exploration in Goal-Conditioned {RL}},
    author  = {Anonymous},
    booktitle = {Submitted to The Fourteenth International Conference on Learning Representations},
    year    = {2025},
    url     = {https://openreview.net/forum?id=mwgYORsqtv},
    note    = {under review}
}
```

```bibtex
@inproceedings{wang2025,
    title   = {1000 Layer Networks for Self-Supervised {RL}: Scaling Depth Can Enable New Goal-Reaching Capabilities},
    author  = {Kevin Wang and Ishaan Javali and Micha{\l} Bortkiewicz and Tomasz Trzcinski and Benjamin Eysenbach},
    booktitle = {The Thirty-ninth Annual Conference on Neural Information Processing Systems},
    year    = {2025},
    url     = {https://openreview.net/forum?id=s0JVsx3bx1}
}
```

```bibtex
@misc{nimonkar2025selfsupervisedgoalreachingresultsmultiagent,
    title   = {Self-Supervised Goal-Reaching Results in Multi-Agent Cooperation and Exploration},
    author  = {Chirayu Nimonkar and Shlok Shah and Catherine Ji and Benjamin Eysenbach},
    year    = {2025},
    eprint  = {2509.10656},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    url     = {https://arxiv.org/abs/2509.10656},
}
```
