Reinforced Linear Genetic Programming

TL;DR

This paper introduces Reinforced Linear Genetic Programming (RLGP), combining Q-Learning with LGP to automate register-action mapping, supported by a new Rust-based framework for experimentation.

Contribution

It presents a novel RLGP approach that integrates reinforcement learning with LGP, and provides a new Rust framework for flexible experimentation.

Findings

RLGP automates register-action mapping effectively.

The Rust framework enables extensive experimentation.

Potential for improved problem-solving with RLGP.

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. However, there still exists some limitations to the technique, such as the need for humans to explicitly map registers to actions. This thesis proposes a novel approach that uses Q-Learning on top of LGP, Reinforced Linear Genetic Programming (RLGP) to learn the optimal register-action assignments. In doing so, we introduce a new framework "linear-gp" written in memory-safe Rust that allows for extensive experimentation for future works.