Experiential learning, Unsupervised learning, Supervised learning, Classifiers & Machine Learning ...

Supervised learning resembles a structured classroom environment, where explicit feedback is given for each example (e.g., a teacher correcting a student's answers). In contrast, reinforcement learning mirrors experiential learning, where feedback comes as rewards or penalties after actions, guiding behavior toward long-term goals. For instance, a child learning to ride a bike might fall (penalty) or stay balanced (reward), gradually improving through trial and error.

Q-learning is a model-free reinforcement learning algorithm that enables an agent to learn an optimal policy for decision-making. It works by estimating the Q-values (action-value function), which represent the expected cumulative reward for taking an action in a given state and following the best future actions. The agent updates Q-values iteratively using the formula: