用强化学习自动分配寄存器动作，提升线性基因编程效率
Reinforced Linear Genetic Programming
- 在传统线性基因编程上叠加Q-Learning，自动学习寄存器与动作的映射
- 新框架采用内存安全的Rust实现，支持高效实验和后续研究
- 适合对自动化程序生成和强化学习结合感兴趣的读者
线性基因编程（LGP）是一种使用线性程序表示解决多种问题的强大技术。然而该方法仍存在局限，例如需要人工显式地将寄存器映射到动作。本文提出一种新方法——强化线性基因编程（RLGP），在LGP基础上引入Q-Learning，以学习最优的寄存器-动作分配。为此，我们构建了一个名为“linear-gp”的新框架，采用内存安全的Rust语言编写，便于未来研究的广泛实验。
原文摘要 · Abstract (English)
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.
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