问在训练强化学习智能体时，tflearn损失始终为0.0

EN

from tflearn import *
import gym
import numpy as np

env = gym.make('CartPole-v0')
x = []
y = []
max_reward = 0

for i in range(1000):
    env.reset()
    while True:
        action = env.action_space.sample()
        observation, reward, done, info = env.step(action)
        if done:
            break
        if reward >= max_reward:
            x.append(observation)
            y.append(np.array([action]))
x = np.asarray(x)
y = np.asarray(y)

net = input_data((None,4))
net = fully_connected(net,8,'softmax')
net = fully_connected(net,16,'softmax')
net = fully_connected(net,32,'softmax')
net = fully_connected(net,64,'softmax')
net = fully_connected(net,128,'softmax')
net = fully_connected(net,64,'softmax')
net = fully_connected(net,32,'softmax')
net = fully_connected(net,16,'softmax')
net = fully_connected(net,8,'softmax')
net = fully_connected(net,4,'softmax')
net = fully_connected(net,2,'softmax')
net = fully_connected(net,1)
net = regression(net,optimizer='adam',learning_rate=0.01,loss='categorical_crossentropy',batch_size=1)
model = DNN(net)

model.fit(x,y,10)
model.save('saved/model.tflearn')