Q-Learning

Q-Learning is a reinforcement learning algorithm designed to learn an action value function Q, which evaluates the expected returns of taking a specific action in a given state. Its core objective is to enable the agent to make autonomous decisions in various situations, choosing actions that can maximize cumulative rewards, thereby achieving efficient learning and adaptation in dynamic environments. Q-Learning does not require a model of the environment, making it highly generalizable and valuable for applications, and it is widely used in areas such as gaming and robot navigation.