Q-learning is a model-free reinforcement learning algorithm that enables an agent to learn how to optimally act in a given environment by maximizing cumulative rewards. It works by updating a value function, called the Q-value, which estimates the expected utility of taking a specific action in a specific state. This method allows the agent to learn optimal policies without needing a model of the environment's dynamics.
congrats on reading the definition of q-learning. now let's actually learn it.