Q-learning is a type of reinforcement learning algorithm used in deep learning. It's based on the concept that an agent learns to take actions in an environment by maximizing a reward signal. The algorithm updates the policy through trial and error, adjusting the action-value function to maximize expected returns. Q-learning can be applied to various tasks, including games and robotics. However, it has limitations, such as requiring large amounts of data and being sensitive to exploration-exploitation trade-offs. --- Why it matters: Understanding Q-learning is crucial for AI researchers and engineers working on reinforcement learning applications, as it provides a foundation for developing more complex algorithms and improving the performance of deep learning models in tasks like game playing and robotics. Source: https://huggingface.co/blog/deep-rl-q-part2