Q-learning is one of the most fundamental algorithms in reinforcement learning, enabling machines to learn optimal decisions through interaction and experience. By combining mathematical foundations like Markov Decision Processes and the Bellman equation, it transforms simple trial-and-error into intelligent behavior. This blog explores how Q-learning works, why it matters, and how it drives modern AI systems.
Learn reinforcement learning in Python through a hands-on Q-learning example. Build a GridWorld environment, train an agent, and visualize how it learns optimal decisions step by step.
