@@ -17,9 +17,9 @@ By using reinforcement learning and a simulator (the game), you can learn how to
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 walk through this lesson to build.
You can open [the lesson notebook](https://github.com/microsoft/ML-For-Beginners/blob/main/8-Reinforcement/1-QLearning/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.
> **Note:** If you are opening this code from the cloud, you also need to fetch the [`rlboard.py`](https://github.com/microsoft/ML-For-Beginners/blob/main/8-Reinforcement/1-QLearning/rlboard.py) file, which is used in the notebook code. Add it to the same directory as the notebook.
## Introduction
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@@ -41,7 +41,7 @@ Each cell in this board can either be:
* an **apple**, which represents something Peter would be glad to find in order to feed himself.
* a **wolf**, which is dangerous and should be avoided.
There is a separate Python module, [`rlboard.py`](rlboard.py), which contains the code to work with this environment. Because this code is not important for understanding our concepts, we will import the module and use it to create the sample board (code block 1):
There is a separate Python module, [`rlboard.py`](https://github.com/microsoft/ML-For-Beginners/blob/main/8-Reinforcement/1-QLearning/rlboard.py), which contains the code to work with this environment. Because this code is not important for understanding our concepts, we will import the module and use it to create the sample board (code block 1):
