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optimization_specialist_demo.py
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optimization_specialist_demo.py
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###############################################################################
# EvoMan FrameWork - V1.0 2016 #
# DEMO : Neuroevolution - Genetic Algorithm neural network. #
# Author: Karine Miras #
###############################################################################
# imports framework
import sys
sys.path.insert(0, 'evoman')
from environment import Environment
from demo_controller import player_controller
# imports other libs
import time
import numpy as np
from math import fabs,sqrt
import glob, os
experiment_name = 'individual_demo'
if not os.path.exists(experiment_name):
os.makedirs(experiment_name)
n_hidden_neurons = 10
# initializes simulation in individual evolution mode, for single static enemy.
env = Environment(experiment_name=experiment_name,
enemies=[2],
playermode="ai",
player_controller=player_controller(n_hidden_neurons),
enemymode="static",
level=2,
speed="fastest")
# default environment fitness is assumed for experiment
env.state_to_log() # checks environment state
#### Optimization for controller solution (best genotype-weights for 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+1)*5
dom_u = 1
dom_l = -1
npop = 100
gens = 30
mutation = 0.2
last_best = 0
# runs simulation
def simulation(env,x):
f,p,e,t = env.play(pcont=x)
return f
# normalizes
def norm(x, pfit_pop):
if ( max(pfit_pop) - min(pfit_pop) ) > 0:
x_norm = ( x - min(pfit_pop) )/( max(pfit_pop) - min(pfit_pop) )
else:
x_norm = 0
if x_norm <= 0:
x_norm = 0.0000000001
return x_norm
# evaluation
def evaluate(x):
return np.array(list(map(lambda y: simulation(env,y), x)))
# tournament
def tournament(pop):
c1 = np.random.randint(0,pop.shape[0], 1)
c2 = np.random.randint(0,pop.shape[0], 1)
if fit_pop[c1] > fit_pop[c2]:
return pop[c1][0]
else:
return pop[c2][0]
# limits
def limits(x):
if x>dom_u:
return dom_u
elif x<dom_l:
return dom_l
else:
return x
# crossover
def crossover(pop):
total_offspring = np.zeros((0,n_vars))
for p in range(0,pop.shape[0], 2):
p1 = tournament(pop)
p2 = tournament(pop)
n_offspring = np.random.randint(1,3+1, 1)[0]
offspring = np.zeros( (n_offspring, n_vars) )
for f in range(0,n_offspring):
cross_prop = np.random.uniform(0,1)
offspring[f] = p1*cross_prop+p2*(1-cross_prop)
# mutation
for i in range(0,len(offspring[f])):
if np.random.uniform(0 ,1)<=mutation:
offspring[f][i] = offspring[f][i]+np.random.normal(0, 1)
offspring[f] = np.array(list(map(lambda y: limits(y), offspring[f])))
total_offspring = np.vstack((total_offspring, offspring[f]))
return total_offspring
# kills the worst genomes, and replace with new best/random solutions
def doomsday(pop,fit_pop):
worst = int(npop/4) # a quarter of the population
order = np.argsort(fit_pop)
orderasc = order[0:worst]
for o in orderasc:
for j in range(0,n_vars):
pro = np.random.uniform(0,1)
if np.random.uniform(0,1) <= pro:
pop[o][j] = np.random.uniform(dom_l, dom_u) # random dna, uniform dist.
else:
pop[o][j] = pop[order[-1:]][0][j] # dna from best
fit_pop[o]=evaluate([pop[o]])
return pop,fit_pop
# loads file with the best solution for testing
if run_mode =='test':
bsol = np.loadtxt(experiment_name+'/best.txt')
print( '\n RUNNING SAVED BEST SOLUTION \n')
env.update_parameter('speed','normal')
evaluate([bsol])
sys.exit(0)
# initializes population loading old solutions or generating new ones
if not os.path.exists(experiment_name+'/evoman_solstate'):
print( '\nNEW EVOLUTION\n')
pop = np.random.uniform(dom_l, dom_u, (npop, n_vars))
fit_pop = evaluate(pop)
best = np.argmax(fit_pop)
mean = np.mean(fit_pop)
std = np.std(fit_pop)
ini_g = 0
solutions = [pop, fit_pop]
env.update_solutions(solutions)
else:
print( '\nCONTINUING EVOLUTION\n')
env.load_state()
pop = env.solutions[0]
fit_pop = env.solutions[1]
best = np.argmax(fit_pop)
mean = np.mean(fit_pop)
std = np.std(fit_pop)
# finds last generation number
file_aux = open(experiment_name+'/gen.txt','r')
ini_g = int(file_aux.readline())
file_aux.close()
# saves results for first pop
file_aux = open(experiment_name+'/results.txt','a')
file_aux.write('\n\ngen best mean std')
print( '\n GENERATION '+str(ini_g)+' '+str(round(fit_pop[best],6))+' '+str(round(mean,6))+' '+str(round(std,6)))
file_aux.write('\n'+str(ini_g)+' '+str(round(fit_pop[best],6))+' '+str(round(mean,6))+' '+str(round(std,6)) )
file_aux.close()
# evolution
last_sol = fit_pop[best]
notimproved = 0
for i in range(ini_g+1, gens):
offspring = crossover(pop) # crossover
fit_offspring = evaluate(offspring) # evaluation
pop = np.vstack((pop,offspring))
fit_pop = np.append(fit_pop,fit_offspring)
best = np.argmax(fit_pop) #best solution in generation
fit_pop[best] = float(evaluate(np.array([pop[best] ]))[0]) # repeats best eval, for stability issues
best_sol = fit_pop[best]
# selection
fit_pop_cp = fit_pop
fit_pop_norm = np.array(list(map(lambda y: norm(y,fit_pop_cp), fit_pop))) # avoiding negative probabilities, as fitness is ranges from negative numbers
probs = (fit_pop_norm)/(fit_pop_norm).sum()
chosen = np.random.choice(pop.shape[0], npop , p=probs, replace=False)
chosen = np.append(chosen[1:],best)
pop = pop[chosen]
fit_pop = fit_pop[chosen]
# searching new areas
if best_sol <= last_sol:
notimproved += 1
else:
last_sol = best_sol
notimproved = 0
if notimproved >= 15:
file_aux = open(experiment_name+'/results.txt','a')
file_aux.write('\ndoomsday')
file_aux.close()
pop, fit_pop = doomsday(pop,fit_pop)
notimproved = 0
best = np.argmax(fit_pop)
std = np.std(fit_pop)
mean = np.mean(fit_pop)
# saves results
file_aux = open(experiment_name+'/results.txt','a')
print( '\n GENERATION '+str(i)+' '+str(round(fit_pop[best],6))+' '+str(round(mean,6))+' '+str(round(std,6)))
file_aux.write('\n'+str(i)+' '+str(round(fit_pop[best],6))+' '+str(round(mean,6))+' '+str(round(std,6)) )
file_aux.close()
# saves generation number
file_aux = open(experiment_name+'/gen.txt','w')
file_aux.write(str(i))
file_aux.close()
# saves file with the best solution
np.savetxt(experiment_name+'/best.txt',pop[best])
# saves simulation state
solutions = [pop, fit_pop]
env.update_solutions(solutions)
env.save_state()
fim = time.time() # prints total execution time for experiment
print( '\nExecution time: '+str(round((fim-ini)/60))+' minutes \n')
file = open(experiment_name+'/neuroended', 'w') # saves control (simulation has ended) file for bash loop file
file.close()
env.state_to_log() # checks environment state