PyTorch-NEAT

A PyTorch implementation of the NEAT (NeuroEvolution of Augmenting Topologies) method which was originally created by Kenneth O. Stanley as a principled approach to evolving neural networks. Read the paper here.

Experiments

PyTorch-NEAT currently contains three built-in experiments: XOR, Single-Pole Balancing, and Car Mountain Climbing.

XOR Experiment

Run with the command: python xor_run.py Will run up-to 150 generations with an initial population of 150 genomes. When/If a solution is found the solution network will be displayed along with statistics about the trial. Feel free to run for more than one trial - just increase the range in the outer for loop in the xor_run.py file.

Single Pole Balancing

Run with the command: python pole_run.py Will run up-to 150 generations with an initial population of 150 genomes. Runs in the OpenAI gym enviornment. When/If a solution is found the solution network will be displayed along with a rendered evalution in the OpenAI gym.

Car Mountain Climbing Experiment

Run with the command: python mountain_climb_run.py Will run up-to 150 generations with an initial population of 150 genomes. Runs in the OpenAI gym enviornment. When/If a solution is found the solution network will be displayed along with a rendered evalution in the OpenAI gym.

An Experiment's Configuration File

Each experiment requries a configuration file. The XOR experiment config file is broken down here:

Import necessary items.

import torch
import torch.nn as nn
from torch import autograd
from v1.phenotype.feed_forward import FeedForwardNet

A config file consists of a Python class with certain requirnments (detailed in comments below).

class XORConfig:
    # Where to evaluate tensors
    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    # Boolean - print generation stats throughout trial
    VERBOSE = False

    # Number of inputs/outputs each genome should contain
    NUM_INPUTS = 2
    NUM_OUTPUTS = 1
    # Boolean - use a bias node in each genome
    USE_BIAS = True

    # String - which activation function each node will use
    # Note: currently only sigmoid and tanh are available - see v1/activations.py for functions
    ACTIVATION = 'sigmoid'
    # Float - what value to scale the activation function's input by
    # This default value is taken directly from the paper
    SCALE_ACTIVATION = 4.9

    # Float - a solution is defined as having a fitness >= this fitness threshold
    FITNESS_THRESHOLD = 3.9

    # Integer - size of population
    POPULATION_SIZE = 150
    # Integer - max number of generations to be run for
    NUMBER_OF_GENERATIONS = 150
    # Float - an organism is said to be in a species if the genome distance to the model genome of a species is <= this speciation threshold
    SPECIATION_THRESHOLD = 3.0

    # Float between 0.0 and 1.0 - rate at which a connection gene will be mutated
    CONNECTION_MUTATION_RATE = 0.80
    # Float between 0.0 and 1.0 - rate at which a connections weight is perturbed (if connection is to be mutated) 
    CONNECTION_PERTURBATION_RATE = 0.90
    # Float between 0.0 and 1.0 - rate at which a node will randomly be added to a genome
    ADD_NODE_MUTATION_RATE = 0.03
    # Float between 0.0 and 1.0 - rate at which a connection will randomly be added to a genome
    ADD_CONNECTION_MUTATION_RATE = 0.5

    # Float between 0.0 and 1.0 - rate at which a connection, if disabled, will be re-enabled
    CROSSOVER_REENABLE_CONNECTION_GENE_RATE = 0.25

    # Float between 0.0 and 1.0 - Top percentage of species to be saved before mating
    PERCENTAGE_TO_SAVE = 0.30

    # XOR's input and output values
    # Note: it is not always necessary to explicity include these values. Depends on the fitness evaluation.
    # See an OpenAI gym experiment config file for a different fitness evaluation example.
    inputs = list(map(lambda s: autograd.Variable(torch.Tensor([s])), [
        [0, 0],
        [0, 1],
        [1, 0],
        [1, 1]
    ]))

    targets = list(map(lambda s: autograd.Variable(torch.Tensor([s])), [
        [0],
        [1],
        [1],
        [0]
    ]))

It is required for an experiment's configuration class to contain a fitness_fn() method. It takes just one argument - a genome.

    def fitness_fn(self, genome):
        fitness = 4.0  # Max fitness for XOR

        phenotype = FeedForwardNet(genome, self)
        phenotype.to(self.DEVICE)
        criterion = nn.MSELoss()

        for input, target in zip(self.inputs, self.targets):  # 4 training examples
            input, target = input.to(self.DEVICE), target.to(self.DEVICE)

            pred = phenotype(input)
            loss = (float(pred) - float(target)) ** 2
            loss = float(loss)

            fitness -= loss

        return fitness

Feel free to add additional methods for experiment-specific uses.

    def get_preds_and_labels(self, genome):
        phenotype = FeedForwardNet(genome, self)
        phenotype.to(self.DEVICE)

        predictions = []
        labels = []
        for input, target in zip(self.inputs, self.targets):  # 4 training examples
            input, target = input.to(self.DEVICE), target.to(self.DEVICE)

            predictions.append(float(phenotype(input)))
            labels.append(float(target))

Contributors

License: MIT

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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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