Reinforcement Learning and Deep RL Python Theory and Projects - Q-Learning and Q-Table Theory

The video tutorial introduces Q Learning as an efficient solution compared to random solutions. It explains the concept of a Q Table, which is a two-dimensional table used in Q Learning to map actions to states and update based on rewards or punishments. The tutorial covers the structure of the Q Table, the types of actions (explore or exploit), and how these actions affect the Q Table. It also discusses the strategies of exploration and exploitation in reinforcement learning, preparing viewers for coding the Q Learning algorithm in the next video.

What is a Q table and how is it structured?

How many actions and states are mentioned in the context of the Q table?

What does it mean to explore versus exploit in the context of Q Learning?