title: What is the difference between Q-learning and Value Iteration?
body: <p>How is Q-learning different from value iteration in reinforcement learning? </p>
  
  <p>I know Q-learning is model-free and training samples are transitions <code>(s, a, s', r)</code>. But since we know the transitions and the reward for every transition in Q-learning, is it not the same as model-based learning where we know the reward for a state and action pair, and the transitions for every action from a state (be it stochastic or deterministic)? I do not understand the difference.</p>