Q-Learning is a model-free reinforcement learning algorithm that enables an agent to learn the optimal action to take in a given state by estimating the expected cumulative reward, known as the Q-value. The algorithm works by maintaining a Q-table (or Q-function) that maps every state-action pair to its expected future reward, and iteratively updating these values as the agent explores its environment through trial and error.
The core of Q-Learning relies on the Bellman equation, which expresses the relationship between the value of a state-action pair and the values of subsequent states. At each step, the agent observes its current state, selects an action (balancing exploration and exploitation), receives a reward, and updates the Q-value for that state-action pair. Over many iterations, the Q-values converge toward optimal values, allowing the agent to derive a policy that maximizes long-term reward.
While tabular Q-Learning works well for environments with small, discrete state spaces, it becomes impractical for complex, high-dimensional problems. This limitation led to the development of Deep Q-Networks (DQN), which use neural networks to approximate the Q-function. DQN famously achieved superhuman performance on Atari games and marked a major breakthrough in combining deep learning with reinforcement learning, laying the groundwork for many modern AI systems.