gen_count = 100  # for how many generations training will last
mutate_chance = .8  # the odds of an organism being mutated on any given generation
full_mutate_chance = .4  # odds of an organism being replaced by a randomized organism instead of just being tweaked according to the normal distribution
standard_deviations = [
    .05 for i in range(2)
]  # how much each gene is mutated by, follows normal distribution so
gene_ranges = [(-3, 3) for i in range(2)]
pop_size = 20  # number of organisms in the population

time_limit = 10**10  # how long each fitness test will run for before just giving up
tick_length = .2  # how often the physics engine will update, smaller values create more precise simulations but take longer

e = Environment(solids=[
    pe.Circle(pos=[-100, -100]),
    pe.Rect(static=True, pos=[-155, 0], height=300),
    pe.Rect(static=True, pos=[155, 0], height=300),
    pe.Rect(static=True, pos=[0, -155], width=300),
    pe.Rect(static=True, pos=[0, 155], width=300)
],
                g_type='downward',
                g_strength=.2)

# initialize population with random genes
initial_population = []
for i in range(pop_size):
    dna = []
    for gene_range in gene_ranges:
        dna.append(np.random.uniform(gene_range[0], gene_range[1]))
    initial_population.append(Organism(dna))
        velocity[i] = g.input0()[i] * dna[0] + \
                      g.input1()[i] * dna[1] + \
                      g.input2()[i] * dna[2] + \
                      g.input3()[i] * dna[3] + \
                      g.input4()[i] * dna[4] + \
                      g.input5()[i] * dna[5]
    # print('destination', destination)
    # print('barrier_x', barrier_x)
    # print('velocity', velocity)

    if stahp:
        sys.exit()

    e = Environment(solids=[
        pe.Circle(pos=[-100, -100], velocity=velocity),
        pe.Rect(static=True, width=100, pos=[barrier_x, 0]),
        pe.Rect(static=True, pos=[-155, 0], height=300),
        pe.Rect(static=True, pos=[155, 0], height=300),
        pe.Rect(static=True, pos=[0, -155], width=300),
        pe.Rect(static=True, pos=[0, 155], width=300)
    ],
                    g_type='downward',
                    g_strength=.2)

    # run the physics engine until the termination condition is reached (termination function has to be put into the physics_engine.py and return statement corrected)
    # time_limit is set to be virtually infinite because we are not worried about this environment running indefinitely, the ball will always eventually slow down to stop
    end_pos = pe.run_physics_engine(.1, e, 10**10)

    total_distance += pe.distance(destination, end_pos)
    # print('end_pos', end_pos)
    # print('distance', pe.distance(destination, end_pos))
import physics_engine as pe
from random import randrange

# this file is for storing and saving environments so that run_physics_engine.py isn't overrun with many lines of commented-out code

# has an instance variable for everything that can be unique to an environment
class Environment:
    def __init__(self, solids, g_type=None, g_strength=None):
        self.solids = solids
        self.g_type = g_type
        self.g_strength = g_strength

# ball through gap in wall
e15 = Environment(solids=[pe.Circle(),
                           pe.Rect(static=True, pos=[100, -450], height=1000),
                           pe.Rect(static=True, pos=[100, 600], height=1000)],
                   g_type='uniform',
                   g_strength=[0, -9.81])

# NN_PS_1, but without the moon to slingshot off of
e14 = Environment(solids=[pe.Circle(pos=[-100, 0], static=True, mass=100),
                          pe.Circle(pos=[0, 0], velocity=[3.3, 3.78], mass=1, radius=1)],
                  g_type='nonuniform',
                  g_strength=10)

# NN_PS_1, maximize velocity from gravity assist
e13 = Environment(solids=[pe.Circle(pos=[-100, 0], static=True, mass=100),
                          pe.Circle(pos=[0, 0], velocity=[3.3, 3.78], mass=1, radius=1),
                          pe.Circle(pos=[50, 0], velocity=[0, 2.582], mass=20)],
                 g_type='nonuniform',
                 g_strength=10)
Beispiel #4
0
# 6 environments for the LT ML algorithm to use, only initialized with instance variables that will be kept constant
PS_1 = Environment(
    solids=[pe.Circle(static=True),
            pe.Circle(radius=1, pos=[0, 11.01])],
    g_type='nonuniform',
    g_strength=100)
PS_2 = Environment(solids=[
    pe.Circle(static=True, pos=[-100, 0], mass=100),
    pe.Circle(radius=1, pos=[-88.99, 0], mass=1),
    pe.Circle(radius=3, pos=[1, 0], velocity=[0, 3.162])
],
                   g_type='nonuniform',
                   g_strength=10)
TD_1 = Environment(solids=[
    pe.Circle(pos=[1, 1]),
    pe.Rect(static=True, pos=[-155, 0], height=300),
    pe.Rect(static=True, pos=[155, 0], height=300),
    pe.Rect(static=True, pos=[0, -155], width=300),
    pe.Rect(static=True, pos=[0, 155], width=300)
],
                   g_type='downward',
                   g_strength=.2)
TD_2 = Environment(solids=[
    pe.Circle(pos=[-100, -100]),
    pe.Rect(static=True, width=150, pos=[10, 0]),
    pe.Rect(static=True, pos=[-155, 0], height=300),
    pe.Rect(static=True, pos=[155, 0], height=300),
    pe.Rect(static=True, pos=[0, -155], width=300),
    pe.Rect(static=True, pos=[0, 155], width=300)
],
                   g_type='downward',