teacher_step_nums = []
step_nums = []
ep_avgs = [0]
ep_avg = []
for ee in range(episodes):
ob = env.reset()
score_ = 0
steps = 0
t_steps = 0
while True:
steps += 1
action = student.act(ob)
next_ob, reward, done, _ = env.step(action)
score_ += reward
student.build_memory(ob, action, reward, next_ob, done)
ob = next_ob
if done:
if (ee+1) % 20 == 0 or ee == 0:
student.model.save_weights("teacher_student_weights.h5")
if ee == 0:
print "Completed {}/{} with a score of {}.".format(ee+1, episodes, score_)
else:
ep_avgs.append(np.mean(ep_avg))
ep_avg = []
print "Completed {}/{} with a score of {}. Average over the last 20 epochs was {} ({}).".format(ee+1, episodes, score_, ep_avgs[-1], ep_avgs[-1]-ep_avgs[-2])
for i in range(5000):
print "===== {} =====".format(str(i))
ob = env.reset()
sum_r = 0
while True:
act_t = pred(ob[None, :, :, :])[0][0].argmax(
) # Record Teacher Action for Training
act_s = student.predict(np.array(ob[None, :, :, :]) /
255.)[0].argmax() # Students Action
if act_t != act_s:
observations_.append(ob / 255.) # Append Observation
teacher_actions.append(act_t)
ob, r, isOver, info = env.step(act_s) # Take Step
sum_r += r
if isOver:
print "Total Score \t {}".format(str(sum_r))
break
scrambled_idx = np.random.choice(len(np.array(observations_)),
size=(len(np.array(observations_)), ),
replace=False)
print "Training Count \t {}".format(str(len(scrambled_idx)))
student.fit(np.array(observations_)[scrambled_idx],
np_utils.to_categorical(teacher_actions,
num_classes=9)[scrambled_idx],
epochs=5,
batch_size=8,