net_frame.py 文件源码

def _action_norm_dist(inpt, num_actions, w_init, activation_fn_v, activation_fn_a):
    mu = layers.fully_connected(inpt, num_outputs=num_actions, weights_initializer=w_init, activation_fn=activation_fn_v)
    sigma = layers.fully_connected(inpt, num_outputs=num_actions, weights_initializer=w_init, activation_fn=activation_fn_a)
    return mu, sigma



# # cnn network frame
# def cnn_frame_continu(hiddens, kerners, strides, inpt, num_actions, scope=None, activation_fn=tf.nn.relu, activation_fn_mu=tf.nn.relu, activation_fn_sigma=tf.nn.relu, reuse=None):
#     with tf.variable_scope(scope, reuse=reuse):
#         out = inpt
#         for kerner, stride in kerners, strides:
#             out = tf.nn.conv2d(input=out, filter=kerner, stride=stride)
#         out = layers.flatten(out)
#         with tf.name_scope("out"):
#             mu = layers.fully_connected(out, num_outputs=num_actions, weights_initializer=tf.truncated_normal_initializer(0 , 0.3), activation_fn=None)
#             sigma = layers.fully_connected(out, num_outputs=num_actions, weights_initializer=tf.truncated_normal_initializer(0 , 0.3), activation_fn=tf.nn.softplus)
#         return mu, sigma