def start_env():
# initializes simulation for coevolution evolution mode.
env = Environment(experiment_name="test",
enemies=enemy,
multiplemode="yes",
playermode="ai",
player_controller=player_controller(),
enemy_controller=enemy_controller(),
level=2,
logs="off",
speed="fastest")
# If model learning is enabled
if use_model:
MODEL = get_model()
# System settings
n_hidden = 10
n_vars = (env.get_num_sensors() +
1) * n_hidden + (n_hidden + 1) * 5 # multilayer with 50 neurons
env.update_parameter('contacthurt', 'player')
return env, n_vars
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.insert(0, 'evoman')
from evoman.environment import Environment
from demo_controller import player_controller, enemy_controller
from random import sample
ENV = Environment(experiment_name="test",
enemies=[2],
playermode="ai",
player_controller=player_controller(),
enemy_controller=enemy_controller(),
level=2,
speed="fastest",
contacthurt='player',
logs='off')
class Individual:
dom_u = 1
dom_l = -1
mutation_rate = .3
n_hidden = 10
n_vars = (ENV.get_num_sensors() + 1) * n_hidden + (n_hidden + 1) * 5 # multilayer with 50 neurons
def __init__(self):
self.age = 0
self.weights = list()
self.fitness = None
self.enemy_life = None
self.get_time())
experiment_name = 'co_demo'
if not os.path.exists(experiment_name):
os.makedirs(experiment_name)
n_hidden_neurons = 10
# initializes simulation for coevolution evolution mode.
env = environm(experiment_name=experiment_name,
enemies=[2],
playermode="ai",
enemymode="ai",
player_controller=player_controller(n_hidden_neurons),
enemy_controller=enemy_controller(n_hidden_neurons),
level=2,
speed="fastest")
env.state_to_log() # checks environment state
#### Optimization for controller solution (best genotype/weights for perceptron phenotype network): Ganetic Algorihm ###
ini = time.time() # sets time marker
# genetic algorithm params
run_mode = 'train' # train or test
# number of weights for multilayer with 10 hidden neurons
n_vars = (env.get_num_sensors() + 1) * n_hidden_neurons + (n_hidden_neurons +