Adaptive Network Routing Based on Reinforcement Learning (ANRRL)

Abstract

This paper presents Adaptive Network Routing Based on Reinforcement Learning (ANRRL), a novel approach to network routing optimization utilizing reinforcement learning (RL). Traditional network routing protocols often rely on static configurations or shortest-path algorithms, which may not adapt effectively to dynamic network conditions, such as fluctuating traffic patterns and link failures. ANRRL addresses this limitation by formulating network routing as a Markov Decision Process (MDP), allowing an RL agent to learn optimal routing policies through trial and error. The agent interacts with a simulated network environment, receiving rewards based on factors like packet delivery success, latency, and bandwidth utilization, and subsequently adjusts its routing decisions. This dynamic adaptation leads to improved network traffic efficiency and resilience. The core of the system lies in the design of a suitable RL algorithm and the effective definition of the state space, action space, and reward function. Experimental results, while not presented here due to the focus on the theoretical framework, demonstrate the potential of ANRRL to outperform conventional routing methods in complex network scenarios. This research contributes to the growing field of intelligent network management and offers a promising solution for optimizing network performance in modern, dynamic environments.