Objective
To develop an intelligent traffic routing system using Reinforcement Learning (RL) that dynamically optimizes network path selection based on real-time conditions. This project demonstrates how RL agents can learn optimal routing policies to minimize latency, maximize throughput, and balance network load.
Business Value
- Dynamic Optimization: Adapt routing decisions in real-time based on network conditions
- Improved Performance: Minimize latency and maximize throughput through intelligent path selection
- Load Balancing: Automatically distribute traffic to prevent congestion hotspots
- Cost Efficiency: Optimize bandwidth utilization and reduce infrastructure costs
- Automated Decision Making: Replace manual routing adjustments with data-driven automation
Core Libraries
- gym: OpenAI Gym environment for RL training
- stable-baselines3: State-of-the-art RL algorithms (PPO, A2C, DQN)
- networkx: Network topology modeling and analysis
- numpy: Numerical computing for environment simulation
- matplotlib: Visualization of network topology and learning curves
Technical Approach
Model: Deep Q-Network (DQN) or Proximal Policy Optimization (PPO)- Environment: Custom network topology with dynamic traffic loads
- State Space: Network conditions (link utilization, latency, packet loss)
- Action Space: Routing decisions (next hop selection)
- Reward Function: Based on QoS metrics (latency, throughput, packet loss)
Key Features
- Custom network environment simulation
- Multi-objective optimization (latency, throughput, reliability)
- Dynamic traffic pattern adaptation
- Policy visualization and interpretation
- Performance comparison with traditional routing algorithms
Files Structure
007_Intelligent_Traffic_Routing_RL/
├── README.md # This guide
├── notebook.ipynb # RL implementation
├── requirements.txt # Dependencies
└── network_env.py # Custom RL environment