title: Q-learning vs temporal-difference vs model-based reinforcement learning
body: <p>I'm in a course called "Intelligent Machines" at the university. We were introduced with 3 methods of reinforced learning, and with those we were given the intuition of when to use them, and I quote:</p>
  
  <ol>
  <li>Q-Learning - Best when MDP can't be solved. </li>
  <li>Temporal Difference Learning - best when MDP is known or can be learned but can't be solved.</li>
  <li>Model-based - best when MDP can't be learned.</li>
  </ol>
  
  <p>Are there any good examples explaining when to choose one method over the other?</p>