Intelligent Resource Allocation via Deep RL

Deep Q-Networks for wireless network resource allocation and optimization.

This project applies Deep Reinforcement Learning — specifically Deep Q-Networks (DQN) — to the problem of intelligent resource allocation in wireless communication networks.

Approach

  • Formulation of wireless resource allocation as a Markov Decision Process (MDP)
  • DQN-based agent for dynamic spectrum allocation
  • Comparison with classical optimization baselines

Technologies

  • Python, PyTorch
  • OpenAI Gym-style environments
  • Wireless network simulation