neat-rs

A neuroevolution framework written in rust.

How to use

Here is how to train a cart pole balancing neural network, available in the examples/ dir.

The system only has 3 parameters:

• Number of input neurons
• Number of output neurons
• The fitness function that returns a f64

let mut system = NEAT::new(4, 1, |network| {
    let num_simulations = 10;
    let max_steps = 1000;
    let mut env = CartPole::new();

    let mut steps_done = 0;
    let mut fitness = 0.;

    for _ in 0..num_simulations {
        env.reset();

        for _ in 0..max_steps {
            if env.done() {
                break;
            }

            let state = env.state();
            let network_output = network.forward_pass(state.to_vec());
            let env_input = f64::max(-1., f64::min(1., *network_output.first().unwrap()));

            env.step(env_input).unwrap();
            steps_done += 1;
        }

        fitness += env.fitness();
    }

    fitness / num_simulations as f64
});

system.set_configuration(Configuration {
    population_size: 100,
    max_generations: 500,
    stagnation_after: 50,
    node_cost: 1.,
    connection_cost: 1.,
    compatibility_threshold: 2.,
    ..Default::default()
});

system.add_hook(10, |generation, system| {
    println!(
        "Generation {}, best fitness is {}, {} species alive",
        generation,
        system.get_best().2,
        system.species_set.species().len()
    );
});

let (network, fitness) = system.start();

To start the training go to the examples/cart-pole/ dir and run the command below. It will produce a network.bin that contains the neural network "recipe".

cargo run --release -- train

After training, you can see the neural network balancing the pole by running the command below, then dragging the network.bin into the window. That will load and instantiate the neural network. You can apply "wind" with arrow keys.

cargo run --release -- visualize

Things I'd like to add (but probably won't due to the lack of time)

• Two pole balancing task (started it in a different branch)
• Recurrent connections
• Extend the system so it works with both f32 and f64 (might improve performance)
• HyperNEAT
• FS NEAT (feature selection)

Is this useful?

If somebody finds this code useful, or is even willing to fund further development, I'd be happy to talk to you. You can reach me by sending me a message on Twitter.