Q-Learning is a method for training artificial intelligence agents to make decisions in uncertain, dynamic environments. It's a type of machine learning algorithm that falls under the category of reinforcement learning. In reinforcement learning, an agent learns to behave in an environment by performing certain actions and observing the rewards it receives. The method has been successfully applied in a variety of domains, including control systems, robotics, and gaming AI.
Q-Learning specifically works by building a table of values for each state-action pair, called the Q-table. The goal of Q-Learning is to find the optimal policy, which is the best sequence of actions to take in each state to maximize the cumulative reward over time.
To achieve this, the agent tries different actions in each state and updates its Q-table based on the observed rewards. Over time, the agent becomes better at choosing the action with the highest expected reward, leading to improved performance.
