Adaptive Program Code Optimization Based on Reinforcement Learning

Abstract

This paper presents a novel approach to program code optimization leveraging reinforcement learning (RL). Traditional static code analysis techniques often fall short in achieving optimal performance due to their lack of adaptability and global optimization capabilities. Our proposed system utilizes an RL agent to dynamically adjust code optimization strategies based on observed program behavior and runtime characteristics. The agent learns through trial and error, receiving rewards for improved performance metrics (e.g., execution time, memory usage) and penalties for detrimental changes. The system's adaptive nature allows it to effectively tailor optimization strategies to diverse environments and codebases, potentially surpassing the limitations of static analysis methods. We detail the system architecture, the RL algorithm employed (specifically, a Q-learning variant), and the key components involved in the optimization process. The core claim is the construction of an adaptive system capable of automatically optimizing program code performance based on the running environment and code characteristics. The mechanism utilizes reinforcement learning to adjust optimization strategies based on runtime and performance indicators, achieving self-adaptive optimization. This research explores a new paradigm in code optimization, moving beyond static analysis to a dynamic, learning-based approach.