Ryan Dobbs

“Q* is the ideal scenario - it represents the perfect knowledge of the best action to take in every possible state to achieve the highest reward. Q-learning, on the other hand, is the method used to approximate Q*. It’s a learning process where the agent interacts with its environment, receives feedback in terms of rewards or penalties, and updates its Q-values based on that feedback. In Q-learning, the agent initially has no knowledge of the environment and starts with arbitrary Q-values. It then updates these values based on the rewards it receives for its actions, using a formula that takes into account both immediate rewards and estimated future rewards. Over time, through exploration and learning, the Q-values converge to Q*, giving the agent a map of the best actions to take in each state. So, in summary, Q* is the ideal set of Q-values that define the best possible strategy, while Q-learning is the process through which an agent learns and approximates these Q-values based on experience and feedback Or put another way, Q* would be the optimal set of choices to make in a specific game of chess and Q learning would’ve been the method by which those choices were discovered.” ~ GPT-4