tf.reset_default_graph()
# 输入
X = tf.placeholder(tf.float32, shape=input_shape)
in_training_mode = tf.placeholder(tf.bool)
# Q网络
mainQ_weights, mainQ_outputs = QNetwork(X, nActions, nameScope='mainQ')
targetQ_weights, targetQ_outputs = QNetwork(X, nActions, nameScope='targetQ')
# 行为Q
X_action = tf.placeholder(tf.int32, shape=(None, ))
Q_action = tf.reduce_sum(targetQ_outputs*tf.one_hot(X_action, nActions), axis=-1, keep_dims=True)
# 权重复制
copy_op = [tf.assign(main_name, targetQ_weights[var_name]) for var_name, main_name in mainQ_weights.items()]
copy_target_to_main = tf.group(*copy_op)
# 输出
y = tf.placeholder(tf.float32, shape=(None, 1))
# 损失
loss = tf.reduce_mean(tf.square(y - Q_action))
# 预处理器
state_preprocessor = StatePreprocessor(nFrames=nFrames, shape=frame_shape)
# tensorflow会话
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, weights)
in_training_mode = tf.placeholder(tf.bool)
# Q网络
mainQ_weights, mainQ_outputs = QNetwork(X, nActions, nameScope='mainQ')
targetQ_weights, targetQ_outputs = QNetwork(X, nActions, nameScope='targetQ')
# 行为Q
X_action = tf.placeholder(tf.int32, shape=(None, ))
Q_action = tf.reduce_sum(targetQ_outputs * tf.one_hot(X_action, nActions),
axis=-1,
keep_dims=True)
# 权重复制
copy_op = [
tf.assign(main_name, targetQ_weights[var_name])
for var_name, main_name in mainQ_weights.items()
]
copy_target_to_main = tf.group(*copy_op)
# 输出
y = tf.placeholder(tf.float32, shape=(None, 1))
# 损失
loss = tf.reduce_mean(tf.square(y - Q_action))
# 优化器
optimizer = tf.train.AdamOptimizer(learning_rate)
training_op = optimizer.minimize(loss)
# 日志
loss_summary = tf.summary.scalar('loss', loss)
