# if done:
# print('Ep: ', i_episode,
# '| Ep_r: ', round(ep_r, 2))
# ---------- importrant data ----------------------
# ------------- r_d and r_p
# print('r: {:5.1f}, steps: {:3}, r_d: {:5.2f}, r_p: {:5.2f}, d/p: {:-8.3f}'.format( r, steps, r_d, r_p, d_p))
if (done or i == MAX_STEP - 1):
step = i + 1
model.Rewards.append(ep_r)
f = round(t1.now() / step, 3)
print(
'episode: {:4d}, reward: {:5.2f}, r_d: {:5.2f}, r_p: {:5.2f}, steps: {:-6d}, fresh_speed: {}'.format(
i_episode,
round(ep_r, 3),
sum(env.rewards_d), sum(env.rewards_p),
steps,
f,
))
# r = arg.reward_done
# position_ = (0, 0)
# s_ = preprocess_state(s_, position_, env)
# if (arg.show_pre_image and cv_img(s_[-1])): break_flag = 1; break
# tt.sleep(0.5)
break
s = s_
# print('----------------------------- learn ------------------', model.learn_step_counter)
# if done:
# print('Ep: ', i_episode,
# '| Ep_r: ', round(ep_r, 2))
# ---------- importrant data ----------------------
# ------------- r_d and r_p
# print('r: {:5.1f}, steps: {:3}, r_d: {:5.2f}, r_p: {:5.2f}, d/p: {:-8.3f}'.format( r, steps, r_d, r_p, d_p))
if (done or i == MAX_STEP - 1):
step = i + 1
model.Rewards.append(ep_r)
f = round(t1.now() / step, 3)
print(
'episode: {:4d}, reward: {:5.2f}, r_d: {:5.2f}, r_p: {:5.2f}, steps: {:-6d}, fresh_speed: {}'
.format(
i_episode,
round(ep_r, 3),
sum(env.rewards_d),
sum(env.rewards_p),
steps,
f,
))
# r = arg.reward_done
# position_ = (0, 0)
# s_ = preprocess_state(s_, position_, env)
# if (arg.show_pre_image and cv_img(s_[-1])): break_flag = 1; break
# tt.sleep(0.5)
screen = torch.from_numpy(screen)
screen = screen.unsqueeze(0)
screen_np = screen.cpu().squeeze(0).permute(1, 2, 0).numpy()
screen.shape
screen_np.shape
# import matplotlib.pyplot as plt
#
# plt.imshow(screen_np)
# plt.show()
dqn = DQN(h, w, 2) # Height * Width , n_action
tt = Time()
for i in range(100):
dqn.forward(screen)
print(tt.now())
screen.shape
xx = torch.unsqueeze(screen, 0)
torch.cat([screen, screen], -1).shape
l_screen = []
for i in range(100):
l_screen.append(screen)
end = []
for screen0 in l_screen:
screen0 = screen0.unsqueeze(0)
try:
end = torch.cat([end, screen0])
except:
end = screen0