# Q-Learning Recap [[q-learning-recap]]

*Q-Learning* **is the RL algorithm that** :

- Trains a *Q-function*, an **action-value function** encoded, in internal memory, by a *Q-table* **containing all the state-action pair values.**

- Given a state and action, our Q-function **will search its Q-table for the corresponding value.**

- When the training is done, **we have an optimal Q-function, or, equivalently, an optimal Q-table.**

- And if we **have an optimal Q-function**, we
have an optimal policy, since we **know, for each state, the best action to take.**

But, in the beginning, our **Q-table is useless since it gives arbitrary values for each state-action pair (most of the time we initialize the Q-table to 0 values)**. But, as we explore the environment and update our Q-table it will give us a better and better approximation.

This is the Q-Learning pseudocode:

