Q-learning is a type of reinforcement learning algorithm used in deep learning. It's a method for training artificial agents to make decisions based on rewards and penalties. Q-learning works by updating the value function, which estimates the expected return or reward for an action in a given state. The algorithm iteratively updates this function using experience from past interactions with the environment. --- Why it matters: Understanding Q-learning is essential for AI researchers and engineers working on reinforcement learning tasks, as it's a fundamental building block of many deep RL algorithms. Source: https://huggingface.co/blog/deep-rl-q-part1