Learning Path

Python Fundamentals for RL → Proximal Policy Optimization (PPO)

This graduate-level path systematically builds from Markov Decision Processes through dynamic programming, Monte Carlo methods, and temporal-difference learning to modern deep RL algorithms such as DQN and PPO. It emphasizes the mathematical foundations and practical implementation in Python, equipping learners to design, implement, and evaluate RL algorithms.