Add RLJ template

README.md

@@ -46,6 +46,7 @@ Additionally, we are going to provide templates for popular machine learning
 scientific journals as well.
 
 - [Journal of Machine Learning Research (JMLR)](jmlr).
+- [Reinforcement Learning Conference/Journal (RLC/RLJ)](rlj).
 - [Transactions on Machine Learning Research (TMLR)](tmlr).
 
 ## Usage

rlj/README.md

@@ -0,0 +1,84 @@
+# Reinforcement Learning Conference/Conference (RLC/RLJ)
+
+## Usage
+
+You can use this template in the Typst web app by clicking _Start from
+template_ on the dashboard and searching for `pioneering-rlj`. Alternatively,
+you can use the CLI to kick this project off using the command.
+
+```shell
+typst init @preview/pioneering-rlj
+```
+
+Typst will create a new directory with all the files needed to get you started.
+
+## Example Papers
+
+Here are an example paper in [LaTeX][1] and in [Typst][2].
+
+## Configuration
+
+This template exports the `rlj` main styling rule with the following named
+arguments.
+
+- `title`: The paper's title as content.
+- `authors`: An array of author dictionaries. Each of the author dictionaries
+  must have a name key and can have the keys department, organization,
+  location, and email.
+- `keywords`: Publication keywords (used in PDF metadata).
+- `date`: Creation date (used in PDF metadata).
+- `abstract`: The content of a brief summary of the paper or none. Appears at
+  the top under the title.
+- `bibliography`: The result of a call to the bibliography function or none.
+  The function also accepts a single, positional argument for the body of the
+  paper.
+- `appendix`: Content to append after bibliography section.
+- `accepted`: If this is set to `false` then anonymized ready for submission
+  document is produced; `accepted: true` produces camera-redy version. If
+  the argument is set to `none` then preprint version is produced (can be
+  uploaded to arXiv).
+- `summary`: Extended abstract for the cover page.
+- `contributions`: List of contributions for the cover page.
+- `supplementary`: Auxiliary content which was not necessarily subject to peer
+  review.
+
+The template will initialize your package with a sample call to the `rlj`
+function in a show rule. If you want to change an existing project to use this
+template, you can add a show rule at the top of your file.
+
+```typst
+#import "@preview/pioneering-rlj": rlj
+
+#show: rlj.with(
+  title: [Formatting Instructions for RLJ/RLC Submissions],
+  authors: (authors, affls),
+  keywords: (),
+  abstract: [...],
+  bibliography: bibliography("main.bib"),
+  accepted: false,
+  id: none,
+)
+```
+
+## Issues
+
+1. Vertical space between "Abstract" and abstract is `10pt - 1.5ex` but some
+   stretching and shrinking are possible.
+2. Figures, specifically @fig:example, allows a lot of stretching and shrinking
+   of space above and below figure. Typst can not do such transformations at
+   the moment. Thus we manually shrink spaces around the first example figure
+   in order to preset pixel perfect template.
+3. Reference RLC/RLJ template mark comment with `\dagger` but this templates
+   stick to built-in numberings which starts with asterisk.
+4. Line numbering starts from abstract in the reference paper. In this template
+   we enumerate all lines from the beginning of the paper including cover page.
+
+## References
+
++ RLC/RLJ 2024 conference [web site][2025].
++ RLC/RLJ 2025 conference [web site][2025].
+
+[1]: example-paper.latex.pdf
+[2]: example-paper.typst.pdf
+[2024]: https://rl-conference.cc/2024/index.html
+[2025]: https://rl-conference.cc/index.html

rlj/logo.typ

@@ -0,0 +1,30 @@
+/**
+ * logo.typ
+ */
+
+#let letter(baseline: 0pt, size, body) = text(
+  size: size,
+  baseline: baseline,
+  top-edge: "cap-height",
+  bottom-edge: "baseline",
+  body)
+
+#let kern(length) = h(length, weak: true)
+#let TeX =  context {
+  let e = measure(letter(10pt, "E"))
+  let T = "T"
+  let E = text(baseline: 2 * e.height / 6, "E")
+  let X = "X"
+  box(T + kern(-0.1667em) + E + kern(-0.125em) + X)
+}
+#let LaTeX = context {
+  let l = measure(letter(10pt, "L"))
+  let a = measure(letter(7pt, "A"))
+  let L = "L"
+  let A = text(7pt, baseline: a.height - l.height, "A")
+  box(L + kern(-0.36em) + A + kern(-0.15em) + TeX)
+}
+#let LaTeX2e = context {
+  let e = text(baseline: 0.0em, $epsilon$)
+  box(LaTeX + sym.space.sixth + [2] + e)
+}

rlj/main.bib

@@ -0,0 +1,26 @@
+% RLC main.bib Version 2025.0
+
+@book{sutton1998introduction,
+    title={Reinforcement Learning: {A}n Introduction},
+    author={Sutton, Richard S. and Barto, Andrew G.},
+	publisher={The MIT Press},
+	year={1998},
+	address={Cambridge, MA},
+}
+
+@inproceedings{Sutton2000,
+	author="R. S. Sutton and D. McAllester and S. Singh and Y. Mansour",
+	title="Policy Gradient Methods for Reinforcement Learning with Function Approximation",
+	booktitle="Advances in Neural Information Processing Systems 12",
+	year="2000",
+	pages="1057--1063",
+	}
+
+@article{Williams1992,
+	author="R. J. Williams",
+	title="Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning",
+	year="1992",
+	Journal="Machine Learning",
+	volume="8",
+	pages="229--256",
+	}