# Introduction to Reinforcement Learning and Q-Learning
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, 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 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, with the reward being, staying alive and scoring as much points as possible.
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.
[](https://www.youtube.com/watch?v=lDq_en8RNOo)
@ -14,7 +14,7 @@ The point of all this is that by using reinforcement learning, and a simulator (
In this lesson, we will be experimenting with some code in Python. You should be able to run the Jupyter Notebook code from this lesson, either on your computer or somewhere in the cloud.
You can open [the lesson notebook](notebook.ipynb) and continue reading the material there, or continue reading here, and run the code in your favorite Python environment.
You can open [the lesson notebook](notebook.ipynb) and walk through this lesson to build.
> **Note:** If you are opening this code from the cloud, you also need to fetch the [`rlboard.py`](rlboard.py) file, which is used in the notebook code. Add it to the same directory as the notebook.
Jen Looper
3 years ago
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