So continuing the journey on learning RL, I went through the deep Q blog.

https://www.freecodecamp.org/news/an-introduction-to-deep-q-learning-lets-play-doom-54d02d8017d8/

Now this time it required more setup on the colab environment. First, you got to make sure you can install the VizDoom package on colab. There are some dependencies needs to be satisfied. Also make sure you install scikit-image package. This takes about 10 minutes as the package need to be build using CMAKE.

Then I initially just pulled the stock “basic.cfg”, “basic.wad” file of the github site. But found out later that had problems. The big one being the frame was sampled in RGB and give you the wrong dimensions. So now I just download them from the github with the already setup config files. Nevertheless, it helped me to understand what was passed around.

A big thing to learn was, of course, the Deep Q network. Which is in the class “DQNetwork”. I’m more used to program in Keras, so it takes a little more time to understand. You can do this in Keras, although you need to update loss function, which is actually easier to understand to in TensorFlow.

The rest seems straightforward, just let it run and learn. Here is the full colab link.

https://colab.research.google.com/drive/1gZM4pAfH4kroa_44gNYZEE8RDVMiO9cP