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."
def run_game(game,
net,
result_file,
skip_num_frames=0,
max_num_frames=50000,
max_secs_without_reward=50000,
drop_rate=0,
num_evals=1,
display_screen=False):
currnet = cPickle.load(open(net,'r'))
total_fitness = 0
for e in range(num_evals):
currnet.clearCharges()
sim = Simulator(game,
currnet.numInput,
skip_num_frames,
max_num_frames,
max_secs_without_reward,
display)
fitness = 0
i = 0
action = 0
while sim.running():
success = sim.read()
if not success:
break
if sim.reward != 0: print sim.reward
fitness += sim.reward
if np.random.random() >= drop_rate: # activate if signal not dropped
#currnet.clearCharges()
currnet.setInputs(sim.objects)
currnet.activate()
output = currnet.readOutputs()
if len(output) == 18:
action = np.argmax(output)
else:
# compressed repr: output0 is fire; output1-9 are dirs
# relies on output functions with range centered around 0.5, e.g., [0,1]
action = np.argmax(output[1:]) + 1
if action != 1:
# add 8 if (fire) and (dir not noop)
if output[0] >= 0.5:
action += 8
else:
# substract 1 if (not fire) and (dir noop)
if output[0] < 0.5: action -= 1
sim.write('{},18\n'.format(action))
i += 1
total_fitness += fitness
print "TOTAL FITNESS: "+str(total_fitness)
avg_fitness = float(total_fitness)/num_evals
tmp = tempfile.mktemp()
f = open(tmp, 'w')
f.write('{}'.format(avg_fitness))
f.close()
shutil.move(tmp, result_file)
