What is Q-Learning?
Q-Learning — A model-free reinforcement learning algorithm used to find the best action to take given a current state.
Q-Learning builds a table of expected rewards for each action in each state. The agent explores the environment, updates its reward estimates, and gradually learns the optimal strategy. It is foundational to reinforcement learning but does not scale well to problems with large state spaces.
Frequently Asked Questions
Where is Q-Learning used in practice?
Game AI, robotics navigation, simple resource allocation, and as a building block in more advanced RL algorithms like Deep Q-Networks (DQN).
What is the ‘Q’ in Q-Learning?
Q stands for quality — it represents the quality (expected future reward) of taking a specific action in a specific state.
How is Q-Learning related to modern AI?
Q-Learning is foundational but limited. Modern approaches like Deep Q-Networks use neural networks to approximate Q-values, enabling reinforcement learning in complex environments like video games and robotics.