# Super Auto Pets AI - Rust

This is a Rust implementation of an AI (neural network) for the game Super Auto Pets. It is a work in progress.

## Usage

### Running it yourself

You can simply run the AI using the following command:

```console
$ cargo install super-auto-pets-ai
$ super_auto_pets_ai
```

The program will run for 100 generations. It will then print the best neural network to the console.

### Including it in your own code

Alternatively, if you want to use the AI in your own project, you can add it as a dependency in your `Cargo.toml` file:

```toml
[dependencies]
super-auto-pets-ai = "0.1.2"
```

You can then use the AI in your code:

```rust
use super_auto_pets_ai::run;

fn main() {
    run();
}
```

## How does it work?

The AI is based on a genetic algorithm. It uses a neural network to determine the best action to take in a given situation. The neural network is trained using a genetic algorithm. The genetic algorithm uses a population of neural networks. Each neural network is given a score based on how well it performs. The neural networks with the highest scores are used to create the next generation of neural networks. This process is repeated until the neural network with the highest score is found.

Each pet has a neural network. The neural network takes the following inputs:

1. The pet's current health
2. The pet's current attack
3. The current shop tier

Given this information, the neural network will consult the weights of the neural network to determine the best action to take. The neural network will output a value between 0 and 1. The value will be rounded to the nearest integer. The integer will be used to determine the action to take. This could include buying an item, putting a pet in the arena, or doing nothing.

## How do I train my own neural network?

To train your own neural network, you need to create a new file in the `src/ai` directory. The file should be named `ai.rs`. You can copy the contents of `src/ai/example.rs` into your new file. You can then modify the code to train your own neural network.

# What is this all about, really?

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