What's the difference between reinforcement learning, deep learning, and deep reinforcement learning? Where does Q-learning fit in?
Reinforcement learning is about teaching an agent to navigate an environment using rewards. Q-learning is one of the primary reinforcement learning methods.
Deep learning uses neural networks to achieve a certain goal, such as recognizing letters and words from images.
Deep reinforcement learning is a combination of the two, using Q-learning as a base. But instead of using actual state-value pairs, this is often used in environments where the state-action space is so large that it would take too long for Q-learning to converge. By using neural networks, we can find other state-action pairs that are similar. This “function approximation” allows effective learning in environments with very large state-action spaces.