Please explain why abovementioned Q-learning formula does NOT explicitly contain any importance sampling ratios

The Q-learning formula is given by:

Q(s, a) = Q(s, a) + α [r + γ maxa' Q(s', a') - Q(s, a)]

where,

- Q(s, a) is the estimated value of taking action a in state s.
- α is the learning rate, which determines the weight given to new experiences.
- r is the reward obtained after taking action a in state s.
- γ is the discount factor, which determines the importance of future rewards.
- maxa' Q(s', a') is the estimated value of the best action to take in the next state s'.

The Q-learning formula does not explicitly contain any importance sampling ratios because it does not involve sampling from any probability distributions. Q-learning is a model-free reinforcement learning algorithm that estimates the value of actions based solely on the observed rewards and transitions between states. The importance sampling ratio is typically used in other reinforcement learning algorithms, such as Monte Carlo and Temporal Difference learning, which involve sampling from probability distributions to estimate the expected value of future rewards.