As we discussed in Chapter 2, Convolutional and Recurrent Networks, ML-Agents does not use any pooling in order to avoid any loss of spatial relationships in data. However, as we saw in our self-driving vehicle example, a single pooling layer or two up at the higher feature level extraction (convolutional layers) can in fact help. Although our example was tested on a much more complex network, it will be helpful to see how this applies to a more complex ML-Agents CNN embedding. Let's try this out, and apply a layer of pooling to the last example by completing the following exercise:
- Open the models.py file in your Python editor of choice. Visual Studio with the Python data extensions is an excellent platform, and also ...
