Packet filtering firewalls are the backbone of every modern network infrastructure; however, increasing demands for network efficiency pose significant challenges. As security policies grow and evolve in both number and complexity, the order in which rules are organized within the firewall often emerges as a critical performance bottleneck. This thesis tackles the reordering problem with the goal of improving packet processing time while preserving the original security semantics. We frame rule ordering as a sequential decision task and adopt Reinforcement Learning to obtain an adaptive, data-driven reordering that follows live traffic. In particular, we implement a lightweight tabular Q-Learning agent that balances exploration and exploitation and continuously adjusts the relative priority of rules as the distribution of flows changes over time.