> 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.
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 given 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, and that would give you a low numerical score. However, pressing the jump button would let score a point and you would stay 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.
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.
[](https://www.youtube.com/watch?v=lDq_en8RNOo)
Jen Looper
3 years ago