Q-learning Algorithm

Q-learning is a type of reinforcement learning algorithm that helps an agent learn how to make decisions by interacting with its environment. It does this by estimating the value of each action it can take in different situations. The agent receives feedback, often in the form of rewards or penalties, and uses this information to update its understanding of which actions lead to better outcomes. Over time, the agent learns an optimal strategy, allowing it to choose actions that maximize its rewards in various scenarios, much like a person learning from experience to make better choices.