Q-learning

Q-Learning is a type of reinforcement learning algorithm used in artificial intelligence to help computers learn how to make decisions. It works by allowing an agent to take actions in an environment, receiving feedback in the form of rewards or penalties. The agent builds a “Q-table” that estimates the value of actions in different situations. Over time, through trial and error, it learns which actions yield the highest rewards, enabling it to choose the best actions in similar future situations. This process helps the agent to improve its decision-making in a variety of tasks.