hl-gauss-pytorch 0.0.1

HL Gauss - Pytorch

HL Gauss - Pytorch

The proposed Gaussian Histogram Loss (HL-Gauss) proposed by Imani et al. with a few convenient wrappers, in Pytorch.

A team at Deepmind wrote a paper with a lot of positive findings for its use in value-based RL.

Install

$ pip install hl-gauss-pytorch

Usage

import torch
from hl_gauss_pytorch import HLGaussLoss

hl_gauss = HLGaussLoss(0., 5., 32, sigma = 0.5)

logits = torch.randn(1, 16, 32).requires_grad_()
targets = torch.randint(0, 5, (1, 16)).float()

loss = hl_gauss(logits, targets)
loss.backward()

Citations

@article{Imani2024InvestigatingTH,
    title   = {Investigating the Histogram Loss in Regression},
    author  = {Ehsan Imani and Kai Luedemann and Sam Scholnick-Hughes and Esraa Elelimy and Martha White},
    journal = {ArXiv},
    year    = {2024},
    volume  = {abs/2402.13425},
    url     = {https://api.semanticscholar.org/CorpusID:267770096}
}

@inproceedings{Imani2018ImprovingRP,
    title   = {Improving Regression Performance with Distributional Losses},
    author  = {Ehsan Imani and Martha White},
    booktitle = {International Conference on Machine Learning},
    year    = {2018},
    url     = {https://api.semanticscholar.org/CorpusID:48365278}
}

@article{Farebrother2024StopRT,
    title   = {Stop Regressing: Training Value Functions via Classification for Scalable Deep RL},
    author  = {Jesse Farebrother and Jordi Orbay and Quan Ho Vuong and Adrien Ali Taiga and Yevgen Chebotar and Ted Xiao and Alex Irpan and Sergey Levine and Pablo Samuel Castro and Aleksandra Faust and Aviral Kumar and Rishabh Agarwal},
    journal = {ArXiv},
    year   = {2024},
    volume = {abs/2403.03950},
    url    = {https://api.semanticscholar.org/CorpusID:268253088}
}