From 21831a0b32d954919a79a41ef1c1d02199cf2e92 Mon Sep 17 00:00:00 2001 From: fdreyer Date: Tue, 26 Mar 2019 01:04:08 +0000 Subject: [PATCH] added arxiv number --- README.md | 26 +++++++++++++++++--------- src/groomrl/scripts/groomer.py | 2 +- 2 files changed, 18 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 9b7ce23..124c851 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,14 @@ [![DOI](https://zenodo.org/badge/159022917.svg)](https://zenodo.org/badge/latestdoi/159022917) -GroomRL: jet grooming through reinforcement learning -==================================================== +GroomRL +======= -This repository contains the code and results presented in [arXiv:19xx.xxxxx](https://arxiv.org/abs/190x.xxxxx "GroomRL paper"). +This repository contains the code and results presented in +[arXiv:1903.09644](https://arxiv.org/abs/1903.09644 "GroomRL paper"). + +## About + +GroomRL is a reinforcement learning framework to train jet grooming strategies. ## Install GroomRL @@ -20,12 +25,14 @@ the "--target=PREFIX_PATH" flag. This process will copy the `groomrl` program to your environment python path. -We recommend the installation of the GroomRL package using a `miniconda3` environment with the [following packages](https://github.com/JetsGame/groomrl/blob/master/environment.yml). +We recommend the installation of the GroomRL package using a `miniconda3` +environment with the +[configuration specified here](https://github.com/JetsGame/groomrl/blob/master/environment.yml). GroomRL requires the following packages: - python3 - numpy -- [fastjet](http://fastjet.fr/) (compiled with --enable-pyext using g++ and make) +- [fastjet](http://fastjet.fr/) (compiled with --enable-pyext) - gym - matplotlib - pandas @@ -40,14 +47,15 @@ GroomRL requires the following packages: ## Pre-trained models The final models presented in -[arXiv:19xx.xxxxx](https://arxiv.org/abs/190x.xxxxx "GroomRL paper") +[arXiv:1903.09644](https://arxiv.org/abs/1903.09644 "GroomRL paper") are stored in: - results/groomerW_final: GroomRL model trained on W jets. - results/groomerTop_final: GroomRL model trained on top jets. ## Input data -All data used for the final models can be downloaded from the git-lfs repository at https://github.com/JetsGame/data. +All data used for the final models can be downloaded from the git-lfs repository +at https://github.com/JetsGame/data. ## Running the code @@ -78,5 +86,5 @@ which will create a new directory in `` using the datafile name. ## References -* S. Carrazza and F.A. Dreyer, "Jet grooming through reinforcement learning," - [arXiv:19xx.xxxxx](https://arxiv.org/abs/190x.xxxxx "GroomRL paper") +* S. Carrazza and F. A. Dreyer, "Jet grooming through reinforcement learning," + [arXiv:1903.09644](https://arxiv.org/abs/1903.09644 "GroomRL paper") diff --git a/src/groomrl/scripts/groomer.py b/src/groomrl/scripts/groomer.py index 05e4b15..cadf9e0 100644 --- a/src/groomrl/scripts/groomer.py +++ b/src/groomrl/scripts/groomer.py @@ -1,7 +1,7 @@ # This file is part of GroomRL by S. Carrazza and F. A. Dreyer """ - groomer.py: the entry point for the groomrl. + groomer.py: the entry point for groomrl. """ from groomrl.read_data import Jets from groomrl.models import build_and_train_model, load_runcard