AttributeError：'Tensor'對象沒有屬性'append'

Question

我不知道為什么此代碼無法正常工作。 當我將獎勵放入列表時，出現錯誤告訴我尺寸不正確。 我不知道該怎么做。

我正在實施加強型深度Q網絡。 r是一個numpy 2d數組，給出1除以停止點之間的距離。 這樣一來，越近的停靠點將獲得越高的獎勵。

不管我做什么，我都無法獲得平穩運行的獎勵。 我是Tensorflow的新手，所以這可能是由於我對Tensorflow占位符和feed dict之類的經驗不足所致。

在此先感謝您的幫助。

observations = tf.placeholder('float32', shape=[None, num_stops])

game states : r[stop], r[next_stop], r[third_stop]

actions = tf.placeholder('int32',shape=[None]) 

rewards = tf.placeholder('float32',shape=[None])  # +1, -1 with discounts

Y = tf.layers.dense(observations, 200, activation=tf.nn.relu)
Ylogits = tf.layers.dense(Y, num_stops)

sample_op = tf.random.categorical(logits=Ylogits, num_samples=1)

cross_entropies = tf.losses.softmax_cross_entropy(onehot_labels=tf.one_hot  (actions,num_stops), logits=Ylogits)

loss = tf.reduce_sum(rewards * cross_entropies)


optimizer = tf.train.RMSPropOptimizer(learning_rate=0.001, decay=.99)
train_op = optimizer.minimize(loss)




visited_stops = []
steps = 0

with tf.Session() as sess:

    sess.run(tf.global_variables_initializer())

    # Start at a random stop, initialize done to false
    current_stop = random.randint(0, len(r) - 1)
    done = False

    # reset everything    
    while not done: # play a game in x steps   

        observations_list = []
        actions_list = []
        rewards_list = []

        # List all stops and their scores
        observation = r[current_stop]

        # Add the stop to a list of non-visited stops if it isn't
        # already there
        if current_stop not in visited_stops:
            visited_stops.append(current_stop)

        # decide where to go
        action = sess.run(sample_op, feed_dict={observations: [observation]})

        # play it, output next state, reward if we got a point, and whether the game is over
        #game_state, reward, done, info = pong_sim.step(action)
        new_stop = int(action)


        reward = r[current_stop][action]

        if len(visited_stops) == num_stops:
            done = True

        if steps >= BATCH_SIZE:
            done = True

        steps += 1

        observations_list.append(observation)
        actions_list.append(action)
        rewards.append(reward)



        #rewards_list = np.reshape(rewards, [-1, 25])
        current_stop = new_stop

    #processed_rewards = discount_rewards(rewards, args.gamma)
    #processed_rewards = normalize_rewards(rewards, args.gamma)

    print(rewards)
    sess.run(train_op, feed_dict={observations: [observations_list],
                             actions: [actions_list],
                             rewards: [rewards_list]})

Answer 1

行rewards.append(reward)導致錯誤，一個是因為你rewards變量是一個張量，只要在限定它rewards = tf.placeholder('float32',shape=[None])和你不能追加值像這樣張量 您可能想調用rewards_list.append(reward) 。

另外，您正在初始化變量

observations_list = []
actions_list = []
rewards_list = []

在循環內，因此在每次迭代中，ols值將被空列表覆蓋。 您可能希望while not done:之前先獲得這3行while not done: