Contrastive RL
Project description
contrastive-rl (wip)
For following a new line of research that started in 2022 from Eysenbach et al.
install
$ pip install contrastive-rl-pytorch
usage
import torch
from contrastive_rl_pytorch import ContrastiveRLTrainer
from x_mlps_pytorch import MLP
encoder = MLP(16, 256, 128)
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')
citations
@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},
}
@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},
}
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