Part 5/11:
Q-learning is a well-established machine learning technique that models decision-making under uncertainty. It involves an agent interacting with an environment, choosing actions based on Q-values—estimates of the potential reward from each action—and updating these estimates as it learns which actions lead to the best outcomes.
The analogy often used is akin to navigating traffic: if you hit a traffic jam, your "frustration" can be seen as a negative reward, prompting you to seek alternative routes. Over time, Q-learning helps AI agents learn optimal strategies to reach their goals efficiently, even in unpredictable or complex environments.