What is Q-learning and how does it work?

What is Q-learning and how does it work?

Q-learning is a model-free reinforcement learning algorithm that helps an agent learn how to optimally act in a given environment.

Can you explain the Q-learning formula?

Sure! The Q-learning formula is: Q(s, a) = Q(s, a) + α[r + γ max Q(s', a') - Q(s, a)], where α is the learning rate, γ is the discount factor, and r is the reward.

What are the key components of Q-learning?

The key components are the Q-table, states, actions, rewards, learning rate, and discount factor.

How does Q-learning differ from other reinforcement learning methods?

Q-learning is off-policy, meaning it learns the value of the optimal policy independently of the agent's actions, unlike on-policy methods.

Can you give an example of Q-learning in action?

Certainly! Imagine a robot learning to navigate a maze. It uses Q-learning to update its Q-values based on the rewards it receives for reaching the exit.