def do_trial():
global observation, reward, t, img, action, done, info, avg_reward
observation = env.reset()
net.Flush()
f = 0
for t in range(300):
if render_during_training:
time.sleep(0.01)
env.render()
# interact with NN
inp = interact_with_nn()
if render_during_training:
img = viz.Draw(net)
cv2.imshow("current best", img)
cv2.waitKey(1)
action = np.argmax(out)
observation, reward, done, info = env.step(action)
if done: break
f += reward
avg_reward += f
return avg_reward
def do_trial(env, net, render_during_training):
observation = env.reset()
net.Flush()
f = 0
for t in range(500):
if render_during_training:
#time.sleep(0.001)
env.render()
# interact with NN
interact_with_nn(env, net, t, observation)
if render_during_training:
img = viz.Draw(net)
cv2.imshow("current best", img)
cv2.waitKey(1)
action = np.array(out)
observation, reward, done, info = env.step(action)
f += reward
return f
try:
observation = env.reset()
net = NEAT.NeuralNetwork()
g = pickle.loads(hof[-1])
g.BuildPhenotype(net)
reward = 0
for t in range(250):
time.sleep(0.01)
env.render()
# interact with NN
interact_with_nn()
# render NN
img = viz.Draw(net)
cv2.imshow("current best", img)
cv2.waitKey(1)
action = np.argmax(out)
observation, reward, done, info = env.step(action)
if done:
break
except Exception as ex:
print(ex)
time.sleep(0.2)
