import multiprocessing
import random
from neuralNetwork import NeuralNetwork
from environment import Environment
num_generations = 10000
networks_per_generation = 1000
steps_per_network = 100
core = 4
best_network = NeuralNetwork()
best_network.generate(36, 36, 4)
random.seed()
best_net_steps = 0
best_net_clean = 0
def evolution_step():
possible_values = [-1, 1]
change = random.random() > .7
if change:
return random.choice(possible_values)
return 0
def signals_to_movement(signals):
movement_x = signals[0] - signals[1]
movement_y = signals[2] - signals[3]
if movement_x > movement_y:
return 'right' if movement_x > 0 else 'left'
elif movement_y > movement_x:
import multiprocessing
import random
from neuralNetwork import NeuralNetwork
from environment import Environment
num_generations = 10000
networks_per_generation = 1000
steps_per_network = 100
core = 4
best_network = NeuralNetwork()
best_network.generate(36, 36, 4)
random.seed()
best_net_steps = 0
best_net_clean = 0
def evolution_step():
possible_values = [-1, 1]
change = random.random() > .7
if change:
return random.choice(possible_values)
return 0
def signals_to_movement(signals):
movement_x = signals[0] - signals[1]
movement_y = signals[2] - signals[3]
if movement_x > movement_y:
return 'right' if movement_x > 0 else 'left'
elif movement_y > movement_x:
