openai観測でTensorflowニューラルネットワークをトレーニング

Question

私はTensorflowで経験再生を訓練するNNを作ろうとしていますが、私は修正できないというエラーに遭遇しました。ここに私のコードです：

import gym 
import tensorflow as tf 
import numpy as np 

enviroment = 'CartPole-v0' 
env = gym.make(enviroment) 
x_train = [] 
y_train = [] 
max_reward = 1 

for i_episode in range(100): 
observation = env.reset() 
for i in range(100): 
env.render() 
action = env.action_space.sample() 
observation, reward, done, info = env.step(action) 
if reward >= max_reward: 
x_train.append(observation) 
y_train.append(action) 
max_reward = reward 
if done: 
break 

n_hidden_1 = 50 
n_hidden_2 = 50 
n_input = 4 
n_classes = 1 

x = tf.placeholder("float") 
y = tf.placeholder("float") 

def multilayer_perceptron(x): 
weights = { 
'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])), 
'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), 
'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes])) 
} 
biases = { 
'b1': tf.Variable(tf.random_normal([n_hidden_1])), 
'b2': tf.Variable(tf.random_normal([n_hidden_2])), 
'out': tf.Variable(tf.random_normal([n_classes])) 
} 

layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) 
layer_1 = tf.nn.relu(layer_1) 

layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) 
layer_2 = tf.nn.relu(layer_2) 

out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] 
return out_layer 
prediction = multilayer_perceptron(x) 

loss = tf.reduce_sum(tf.square(prediction-y)) 
optimizer = tf.train.GradientDescentOptimizer(0.001).minimize(loss) 
init = tf.global_variables_initializer() 
sess = tf.Session() 
sess.run(init) 
for i in range(100): 
sess.run(optimizer, {x:x_train, y:y_train}) 

with tf.Session() as sess: 
sess.run(init) 
env = gym.make(enviroment) 
observation = env.reset 
for t in range(1000): 
env.render() 
action = np.around(multilayer_perceptron(observation)) 
observation, reward, done, info = env.step(action) 
if done: 
break 

そして、ここでエラーです：いただきました私のコードで間違ってあなたが私に言うならば、私は非常に感謝

Traceback (most recent call last): 
File "D:\Kay\AI\Q-learning\Q-Learn.py", line 69, in 
action = np.around(multilayer_perceptron(observation)) 
File "D:\Kay\AI\Q-learning\Q-Learn.py", line 44, in multilayer_perceptron 
layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) 
File "C:\Users\Katharina\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\math_ops.py", line 1683, in matmul 
a = ops.convert_to_tensor(a, name="a") 
File "C:\Users\Katharina\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 669, in convert_to_tensor 
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref) 
File "C:\Users\Katharina\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\constant_op.py", line 176, in constanttensor_conversion_function 
return constant(v, dtype=dtype, name=name) 
File "C:\Users\Katharina\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\constant_op.py", line 165, in constant 
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape)) 
File "C:\Users\Katharina\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\tensor_util.py", line 441, in make_tensor_proto 
tensor_proto.string_val.extend([compat.as_bytes(x) for x in proto_values]) 
File "C:\Users\Katharina\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\tensor_util.py", line 441, in 
tensor_proto.string_val.extend([compat.as_bytes(x) for x in proto_values]) 
File "C:\Users\Katharina\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\util\compat.py", line 65, in as_bytes 
(bytes_or_text,)) 
TypeError: Expected binary or unicode string, got <bound method Env.reset of > 

。ありがとう。

Answer 1

ある時点であなたはobservation = env.resetと言っています。 observation = env.reset()（かっこに注意してください）