QoS Optimization with RL

Project Overview

This project explores Reinforcement Learning (RL) to optimize Quality of Service (QoS) parameters in network management. We'll learn how to create and train RL agents that can make dynamic decisions to maintain optimal network performance.

Key Learning Objectives

• Understanding RL fundamentals
• Implementing RL environments
• Policy optimization
• Reward function design
• Agent training and evaluation

Technologies Used

• Python
• Gymnasium (OpenAI Gym)
• Stable-Baselines3
• PyTorch
• TensorBoard for visualization

Additional Resources

For detailed implementation guide, code examples, and step-by-step instructions, please visit the GitHub Project Repository.