Learning Path

Probability Theory Fundamentals → Project: Implement an RL Agent

This learning path guides students through the fundamental concepts and algorithms of reinforcement learning, starting from the mathematical prerequisites and building up to core RL algorithms such as Q-learning and policy gradients. It emphasizes the underlying probabilistic and algorithmic foundations, ensuring a solid understanding of how agents learn optimal behaviors through interaction with their environment.