# 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.**

<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/Q-function-2.jpg" alt="Q function"  width="100%"/>

- 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.**

<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/link-value-policy.jpg" alt="Link value policy"  width="100%"/>

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.

<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/unit2/q-learning.jpeg" alt="q-learning.jpeg" width="100%"/>

This is the Q-Learning pseudocode:

<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/Q-learning-2.jpg" alt="Q-Learning" width="100%"/>
