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 # Introduction to Reinforcement Learning and Q-Learning
 
+![Summary of reinforcement in machine learning in a sketchnote](../../sketchnotes/ml-reinforcement.png)
+> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
 Reinforcement learning involves three important concepts: the agent, some states, and a set of actions per state. By executing an action in a specified state, the agent is scored with a reward. Again imagine the computer game Super Mario. You are Mario, you are in a game level, standing next to a cliff edge. Above you is a coin. You being Mario, in a game level, at a specific position ... that's your state. Moving one step to the right (an action) will take you over the edge, that would give you a low numerical score. However, pressing the jump button, you would score a point and you would be alive. That's a positive outcome and that should award you a positive numerical score.
 
 The point of all this is that by using reinforcement learning and a simulator (the game), you can learn how to play the game to maximize the reward which is staying alive and scoring as many points as possible.