agent = Agent('eval/prim.pth')
#save path
save_path='prim_result/'
utils.check_dirs([save_path])
shape_infopack=['demo', 'eval/demo-16.binvox', 'eval/demo-64.binvox', 'rgb', 'demo.png']
s, box, step = env.reset(shape_infopack)
acm_r=0
step_interval=20
while True:
valid_mask = env.get_valid_action_mask(box)
a = agent.choose_action(s, box, step, valid_mask, 1.0)
s_, box_, step_, r, done = env.next(a)
acm_r+=r
if env.step_count%step_interval==0:
log_info='demo_step_'+str(env.step_count)
print(log_info)
env.output_result(log_info, save_path)
if done:
log_info='demo_finish_reward_'+str(acm_r)
print(log_info)
env.output_result(log_info, save_path)
env.save_edgeloop(save_path)
break
n_total = 0
for n in range(n_games):
if not n == 0:
scores.append(score)
avg_rewards.append(total_reward / n_moves)
agent.learn(n_total, n_total + n_moves)
score = 0
game.score = 0
total_reward = 0
n_moves = 0
n_total = n_moves+n_total
done = False
observation = game.get_states()
while not done:
action = agent.choose_action(observation)
if n < -1:
game.draw()
action = int(msvcrt.getch().decode("utf-8"))
observation_, reward, done, score = game.spin_once(action, n_moves)
agent.store_data(observation, action, reward, observation_, done)
observation = observation_
total_reward += reward
n_moves += 1
#game.draw()
if score > score_max:
print(score)
score_max = score
print(f"max score: {score_max}")
if n % 100 == 0:
# alpha beta tau batch_size
score_record = []
score_record_step = []
count_record = []
count_record_step = []
time_record = []
time_record_step = []
for i in range(800):
done = False
score = 0
obs = env.get_init_state()
# 没分配完
while not done:
act = MECSnet.choose_action(obs)
new_state, reward, done = env.step(act)
MECSnet.remember(obs, act, reward, new_state, int(done))
MECSnet.learn()
score += reward
obs = new_state
# print('reward is： {}'.format(reward))
# 本轮的reward追加到list中
score_record.append(score)
# print('episode ', i, 'score %.2f' % score, '100 game average %.2f' % np.mean(score[-100:]))
print('episode ', i, 'score %.2f' % score, " wrong: ", env.count_wrong)
count_record.append(1 - env.count_wrong / num_task)
time_record.append(env.time)
if i % 25 == 0:
# UEnet.save_models()