##基于深度强化学习的自适应网络路由

This paper presents a novel approach to network routing utilizing Deep Reinforcement Learning (DRL). Traditional network routing protocols rely heavily on static rules and pre-defined paths, often failing to adapt effectively to dynamic network conditions such as fluctuating traffic demands and network topology changes. This research proposes a DRL-based adaptive routing system designed to learn optimal routing policies directly from network simulations. The system employs a deep neural network as a reinforcement learning agent, trained to navigate a simulated network environment. The agent learns to select routes based on a reward function that optimizes for metrics like latency, throughput, and packet loss. The key innovation lies in the agent's ability to dynamically adjust its routing decisions in real-time, reacting to changes in network state. This adaptive routing significantly enhances network performance and resilience, addressing limitations inherent in conventional routing methods. The proposed framework demonstrates a promising pathway for intelligent network management and offers a scalable solution for optimizing network traffic flow. ---