PostgreSQL Database Parameter Optimization Based on Reinforcement Learning

Traditional methods for configuring PostgreSQL database parameters mainly rely on manual experience and trial and error, which are inefficient and difficult to find the optimal configuration. This paper proposes a method based on reinforcement learning to optimize PostgreSQL database parameters. By using reward-based feedback mechanisms to guide the decision-making of the agent, it gradually adjusts and finds the optimal parameter configuration. The results of experiment show that this method can enhance the performance of PostgreSQL databases under various workloads effectively.