def evolve_atari_network(game, input_size):
ne = NE.GSP(input_size,18,2,[],True)
NUM_GENERATIONS = 1000
best_fitness = -10000000
best_net = None
generation = 0
while generation < NUM_GENERATIONS:
curr_best_fitness = -10000000
# test each subpopulation
for i in range(ne.populationSize):
sim = Simulator(game)
currnet = ne.testNet(i)
fitness = 0
while sim.running():
sim.read()
fitness += sim.reward
#print "{}, ".format(fitness),
currnet.clearCharges()
currnet.setInputs(sim.objects.reshape(input_size*80,1))
currnet.activate()
output = currnet.readOutputs()
sim.write('{},18\n'.format(np.argmax(output)))
ne.evaluate(fitness, i)
if fitness > curr_best_fitness:
curr_best_fitness = fitness
#currnet.visualize()
if fitness > best_fitness:
best_fitness = fitness
pickle.dump(currnet, open('nets/{}.net'.format(game),'wb'))
#best_net = copy.deepcopy(currnet)
#best_net.visualize()
print "gen: {}\ti: {}\trew: {}\tbest: {}\t end: {}" \
.format(generation, i, fitness,
best_fitness, sim.terminated)
sim.kill()
with open('nets/{}.curve'.format(game),'a') as curve:
curve.write(str(curr_best_fitness)+',')
print "Gen " + str(generation) + ", Best: " + str(curr_best_fitness)
ne.nextGen()
generation += 1
print "Generation "+str(generation)+", task complete."
