Can you explain Q-learning?

Can you explain Q-learning?

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

How does Q-learning work?

Q-learning works by learning a value function that estimates the expected utility of taking a given action in a given state.

What are the key components of Q-learning?

The key components include the Q-table, learning rate, discount factor, and exploration strategy.

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

Absolutely! 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.