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RLLTE: Long-Term Evolution Project of Reinforcement Learning

Mingqi Yuan, Zequn Zhang, Yang Xu, Shihao Luo, Bo Li, Xin Jin, Wenjun Zeng

TL;DR

This document provides comprehensive guidelines for AAAI 2025 submission and camera-ready formatting, covering anonymous submission protocols, copyright requirements, and detailed LaTeX-based formatting rules. It specifies strict requirements on fonts, layout, and structure, including two-column US Letter formatting, embedded fonts, and prohibition of layout-altering commands. It also outlines file submission expectations, figure and table handling, references, and optional sections such as Ethical Statements and Acknowledgments. The guidelines aim to ensure consistent, high-quality publications and to streamline editorial workflows across authors and editors. Overall, it functions as a exhaustive manual for producing publication-ready AAAI submissions with minimal deviations from the standard style.

Abstract

We present RLLTE: a long-term evolution, extremely modular, and open-source framework for reinforcement learning (RL) research and application. Beyond delivering top-notch algorithm implementations, RLLTE also serves as a toolkit for developing algorithms. More specifically, RLLTE decouples the RL algorithms completely from the exploitation-exploration perspective, providing a large number of components to accelerate algorithm development and evolution. In particular, RLLTE is the first RL framework to build a comprehensive ecosystem, which includes model training, evaluation, deployment, benchmark hub, and large language model (LLM)-empowered copilot. RLLTE is expected to set standards for RL engineering practice and be highly stimulative for industry and academia. Our documentation, examples, and source code are available at https://github.com/RLE-Foundation/rllte.

RLLTE: Long-Term Evolution Project of Reinforcement Learning

TL;DR

This document provides comprehensive guidelines for AAAI 2025 submission and camera-ready formatting, covering anonymous submission protocols, copyright requirements, and detailed LaTeX-based formatting rules. It specifies strict requirements on fonts, layout, and structure, including two-column US Letter formatting, embedded fonts, and prohibition of layout-altering commands. It also outlines file submission expectations, figure and table handling, references, and optional sections such as Ethical Statements and Acknowledgments. The guidelines aim to ensure consistent, high-quality publications and to streamline editorial workflows across authors and editors. Overall, it functions as a exhaustive manual for producing publication-ready AAAI submissions with minimal deviations from the standard style.

Abstract

We present RLLTE: a long-term evolution, extremely modular, and open-source framework for reinforcement learning (RL) research and application. Beyond delivering top-notch algorithm implementations, RLLTE also serves as a toolkit for developing algorithms. More specifically, RLLTE decouples the RL algorithms completely from the exploitation-exploration perspective, providing a large number of components to accelerate algorithm development and evolution. In particular, RLLTE is the first RL framework to build a comprehensive ecosystem, which includes model training, evaluation, deployment, benchmark hub, and large language model (LLM)-empowered copilot. RLLTE is expected to set standards for RL engineering practice and be highly stimulative for industry and academia. Our documentation, examples, and source code are available at https://github.com/RLE-Foundation/rllte.
Paper Structure (65 sections, 3 figures, 2 tables, 1 algorithm)

This paper contains 65 sections, 3 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Preparing an Anonymous Submission
  2. Camera-Ready Guidelines
  3. Copyright
  4. Formatting Requirements in Brief
  5. What Files to Submit
  6. Using LaTeX to Format Your Paper
  7. Document Preamble
  8. The Following Must Appear in Your Preamble
  9. Preparing Your Paper
  10. Commands and Packages That May Not Be Used
  11. Page Breaks
  12. Paper Size, Margins, and Column Width
  13. Column Width and Margins.
  14. Overlength Papers
  15. Type Font and Size
  16. ...and 50 more sections

Figures (3)

  • Figure 1: Using the trim and clip commands produces fragile layers that can result in disasters (like this one from an actual paper) when the color space is corrected or the PDF combined with others for the final proceedings. Crop your figures properly in a graphics program -- not in LaTeX.
  • Figure 2: Adjusting the bounding box instead of actually removing the unwanted data resulted multiple layers in this paper. It also needlessly increased the PDF size. In this case, the size of the unwanted layer doubled the paper's size, and produced the following surprising results in final production. Crop your figures properly in a graphics program. Don't just alter the bounding box.
  • Figure 3: Example listing quicksort.hs