Understanding Q-Learning

Reinforcement Learning (RL) is how we teach machines to learn from trial and error. Q-Learning is one of the foundational algorithms in this field.

The Q-Table

Imagine a cheat sheet (a table) where:

• Rows are States (situations you can be in).
• Columns are Actions (things you can do).
• Values (Q-Values) represent how "good" an action is in that state.

The Algorithm

The agent starts knowing nothing (all zeros). It explores randomly. When it takes an action and gets a Reward, it updates the Q-Value for that state-action pair using the Bellman Equation:

Q(s,a) = Q(s,a) + alpha * (R + gamma * max(Q(s',a')) - Q(s,a))

Over time, the table fills up with accurate scores, and the agent becomes an expert at maximizing its reward.