Q-Learning

Q-Learning is a model-free reinforcement learning algorithm that enables an agent to learn optimal actions in an environment by iteratively updating a Q-table, which stores the expected cumulative rewards for state-action pairs. It operates without requiring a model of the environment's dynamics, making it widely applicable in scenarios like game playing, robotics, and autonomous systems. The algorithm uses a temporal difference approach to balance exploration and exploitation, converging to an optimal policy over time.

Also known as: Q Learning, Q-Learning Algorithm, Q-Learning RL, Q-Learning Method, Q-Learning TD

Why learn Q-Learning?

Developers should learn Q-Learning when building applications that involve decision-making under uncertainty, such as training AI for games, optimizing resource allocation, or developing autonomous agents in simulated environments. It is particularly useful in discrete state and action spaces where a Q-table can be efficiently maintained, and it serves as a foundational technique for understanding more advanced reinforcement learning methods like Deep Q-Networks (DQN).

Learning Resources

• book: Reinforcement Learning: An Introduction - Sutton & Barto
• tutorial: Q-Learning Tutorial by Towards Data Science
• course: Q-Learning Course on Coursera
• video: Q-Learning Explained - YouTube Video by 3Blue1Brown
• docs: Q-Learning Documentation on OpenAI Gym