rlgp-thesis

LaTeX source for Reinforced Linear Genetic Programming, a BCS honours thesis exploring the integration of Q-Learning with Linear Genetic Programming.

Download PDF · arXiv · Dalhousie · Framework Source

Abstract

Linear Genetic Programming (LGP) is a powerful technique that allows for a variety of problems to be solved using a linear representation of programs. This thesis proposes Reinforced Linear Genetic Programming (RLGP), a novel approach that uses Q-Learning on top of LGP to learn optimal register-action assignments. RLGP is evaluated on the CartPole-v1 and MountainCar-v0 environments from OpenAI Gym using a framework written in Rust.

Building

Prerequisites

• TeX Live (full distribution)
• latexmk

Compile

latexmk -pdf -pdflatex="pdflatex -interaction=nonstopmode" thesis.tex

arXiv Submission Package

bash draft_package.sh

Structure

├── thesis.tex          # Main document
├── thesis.bib          # Bibliography
├── dalcsthesis.cls     # Dalhousie CS thesis class
├── tables/             # Pre-generated longtables (from CSV data)
├── assets/
│   ├── experiments/    # Results and figures
│   └── parameters/     # Hyperparameter configurations
└── draft_package.sh    # arXiv packaging script

Citation

@thesis{mukhammadnaim2023rlgp,
  title  = {Reinforced Linear Genetic Programming},
  author = {Mukhammadnaim, Urmzd},
  school = {Dalhousie University},
  type   = {Bachelor's Honours Thesis},
  year   = {2023}
}