Heady | A utility for creating single-header C libraries

The code seems right, perhaps could be of newlines (\n) characters in element of list. Check the strings of lists.

[EDITED] Following comments, here is how you can do it with the raster library:

The exception means that snakeBody.first() is called without any elements in the queue.

This is because snakeBody in Buttons is another Queue than snakeBody in GameState.

In the Buttons-class:

Ok so let's run through the Logic function with different values of i.

i represents the number of loops to do in the Logic function up to the length of the snake which is given by nTail, however, it starts from 1, not 0 so it skips the first tail segment which is always set to where the head was last frame.

When the snake is just a head nTail = 0

Before you eat food, nTail is 0 and therefore the only part of the logic that runs is:

Possibly:

You have to evaluate if the snake head has the same position as a part of the body:

I'm not surprised your snakes are dying.

Let's take a step back. What is AI exactly? Well, it's a search problem. We're searching through some parameter space to find the set of parameters that solve snake given the current state of the game. You can imagine a space of parameters that has a global minimum: the best possible snake, the snake that makes the fewest mistakes.

All learning algorithms start at some point in this parameters space and attempt to find that global maximum over time. First, let's think about MLPs. MLPs learn by trying a set of weights, computing a loss function, and then taking a step in the direction that would further minimize the loss (gradient descent). It's fairly obvious that an MLP will find a minimum, but whether it can find a good enough minimum is a question and there are a lot of training techniques that exist to improve that chance.

Genetic algorithms, on the other hand, have very poor convergence characteristics. First, let's stop calling these genetic algorithms. Let's call these smorgasbord algorithms instead. A smorgasbord algorithm takes two sets of parameters from two parents, jumbles them, and then yields a new smorgasbord. What makes you think this would be a better smorgasbord than either of the two? What are you minimizing here? How do you know it's approaching anything better? If you attach a loss function, how do you know you're in a space that can actually be minimized?

The point I'm trying to make is that genetic algorithms are unprincipled, unlike nature. Nature does not just put codons in a blender to make a new strand of DNA, but that's exactly what genetic algorithms do. There are techniques to add some time of hill climbing, but still genetic algorithms have tons of problems.

Point is, don't get swept up in the name. Genetic algorithms are simply smorgasbord algorithms. My view is that your approach doesn't work because GAs have no guarantees of converging after infinite iterations and MLPs have no guarantees of converging to a good global minimum.

What to do? Well, a better approach would be to use a learning paradigm that fits your problem. That better approach would be to use reinforcement learning. There's a very good course on Udacity from Georgia Tech on the subject.

The two lines that need code change to check for the correct word are :

Use concat with axis=1 and then flatten Multiindex by f-strings: