Chapter 32: Reinforcement Learning Foundations

Learning Objectives

• Formulate Markov decision processes and implement Gym-compatible environments
• Implement DQN and its variants for discrete action spaces
• Implement policy gradient methods (REINFORCE, PPO) for continuous control
• Apply RL to wireless resource allocation and network optimization problems