def _tech_net(self, data, trainable=True):
with tf.variable_scope("Tech_net"):
# if self.use_norm == True:
# data = layer.batch_norm(data, self.is_training, 'BN')
d1 = layer.dense_layer(data,
256,
"DenseLayer1",
is_training=self.is_training,
trainable=trainable,
norm=self.use_norm)
d2 = layer.dense_layer(d1,
128,
"DenseLayer2",
is_training=self.is_training,
trainable=trainable,
norm=self.use_norm)
dout = layer.dense_layer(d2,
1,
"DenseLayerOut",
func=None,
is_training=self.is_training,
trainable=trainable,
norm=self.use_norm)
scope = tf.get_variable_scope().name
return dout, scope
def build_graph(self, obs_ph, acs_ph, reuse=False):
with tf.variable_scope(self.scope):
if reuse:
tf.get_variable_scope().reuse_variables()
data = tf.concat([obs_ph, acs_ph], axis=1) # concatenate the two input -> form a transition
'''p_h1 = tf.contrib.layers.fully_connected(_input, self.hidden_size, activation_fn=tf.nn.relu)
p_h2 = tf.contrib.layers.fully_connected(p_h1, self.hidden_size, activation_fn=tf.nn.relu)
logits = tf.contrib.layers.fully_connected(p_h2, 1, activation_fn=tf.identity)'''
if self.use_norm:
data = layer.batch_norm(data, self.pop_batch_norm, 'BN')
d1 = layer.dense_layer(data, 128, "DenseLayer1", is_training=self.pop_batch_norm, trainable=True,
norm=self.use_norm)
d2 = layer.dense_layer(d1, 32, "DenseLayer2", is_training=self.pop_batch_norm, trainable=True,
norm=self.use_norm)
dout = layer.dense_layer(d2, 1, "DenseLayer3", func=None,
is_training=self.pop_batch_norm, trainable=True, norm=None)
return dout
def __init__(self, name: str, sess, ob_space, act_space_array, activation=tf.nn.relu):
"""
:param name: string
"""
self.sess = sess
with tf.variable_scope(name):
self.obs = tf.placeholder(dtype=tf.float32, shape=[None, ob_space], name='obs')
with tf.variable_scope('policy_net'):
with tf.variable_scope('controller'):
layer_1 = layer.dense_layer(self.obs, 64, "DenseLayer1", func=activation)
layer_2 = layer.dense_layer(layer_1, 64, "DenseLayer2", func=activation)
# act_space_array = wait, build worker, build pylon,
self.tech_probs = layer.dense_layer(layer_2, act_space_array, "tech_output", func=tf.nn.softmax)
self.tech_act = tf.multinomial(tf.log(self.tech_probs), num_samples=1)
self.tech_act = tf.reshape(self.tech_act, shape=[-1])
with tf.variable_scope('value_net'):
layer_1 = layer.dense_layer(self.obs, 64, "DenseLayer1", func=activation)
layer_2 = layer.dense_layer(layer_1, 64, "DenseLayer2", func=activation)
self.v_preds = layer.dense_layer(layer_2, 1, "DenseLayer4", func=None)
# self.act_stochastic = tf.multinomial(tf.log(self.act_probs), num_samples=1)
# self.act_stochastic = tf.reshape(self.act_stochastic, shape=[-1])
# self.act_deterministic = tf.argmax(self.act_probs, axis=1)
self.scope = tf.get_variable_scope().name
def __init__(self, name: str, sess, ob_space, add_ob_space, act_space_array, add_act_space, freeze_head=True,
activation=tf.nn.relu):
"""
:param name: string
"""
self.sess = sess
self.add_weight = 0.2
self.map_width = 32 if P.use_small_map else 64
self.map_channel = 10
with tf.variable_scope(name):
self.obs = tf.placeholder(dtype=tf.float32, shape=[None, ob_space], name='obs')
self.obs_add = tf.placeholder(dtype=tf.float32, shape=[None, add_ob_space], name='obs_add')
self.obs_map = tf.placeholder(dtype=tf.float32, shape=[None, self.map_width, self.map_width, self.map_channel], name='obs_map')
with tf.variable_scope('policy_net'):
with tf.variable_scope('controller'):
layer_1 = layer.dense_layer(self.obs, 64, "DenseLayer1", func=activation, trainable=not freeze_head)
self.layer_2 = layer.dense_layer(layer_1, 64, "DenseLayer2", func=activation, trainable=not freeze_head)
# attention
#layer_3 = layer.dense_layer(self.obs_add, 64, "DenseLayer3", func=tf.nn.sigmoid)
#layer_2 = layer_2 * layer_3
# add obs
self.layer_3 = layer.dense_layer(self.obs_add, 64, "DenseLayer3", func=activation, initial_type='original')
# map obs
self.layer_4, self.map_variable_scope = ops.simple_resnet(self.obs_map, 18, 64, "Resnet")
# weighted sum
self.layer_5 = (1. - self.add_weight) * self.layer_2 + self.add_weight / 2. * self.layer_3 + self.add_weight / 2. * self.layer_4
self.tech_probs = layer.output_layer(self.layer_5, act_space_array, add_act_space, "tech_output", func=tf.nn.softmax)
self.tech_act = tf.multinomial(tf.log(self.tech_probs), num_samples=1)
self.tech_act = tf.reshape(self.tech_act, shape=[-1])
with tf.variable_scope('value_net'):
layer_1 = layer.dense_layer(self.obs, 64, "DenseLayer1", func=activation, trainable=not freeze_head)
layer_2 = layer.dense_layer(layer_1, 64, "DenseLayer2", func=activation, trainable=not freeze_head)
#layer_3 = layer.dense_layer(self.obs_add, 32, "DenseLayer3", func=activation)
#layer_4 = tf.concat([layer_2, layer_3], 1)
self.v_preds = layer.dense_layer(layer_2, 1, "DenseLayer4", func=None)
#self.v_preds = layer.dense_layer(layer_2, 1, "DenseLayer4_add", func=None)
self.scope = tf.get_variable_scope().name
def sep_policy_value_net(self,
name,
hidden_units=64,
resnet=None,
activation=tf.nn.relu,
freeze_head=False,
initial_type='original',
norm=True,
is_training=True,
reuse=False):
with tf.variable_scope(name, reuse=reuse):
with tf.variable_scope('policy_net'):
with tf.variable_scope('controller'):
print('freeze_head:', freeze_head)
layer_1 = layer.dense_layer(self.obs,
hidden_units,
"DenseLayer1",
norm=norm,
is_training=is_training,
func=activation,
initial_type=initial_type,
trainable=not freeze_head)
self.layer_2 = layer.dense_layer(layer_1,
hidden_units,
"DenseLayer2",
norm=False,
is_training=is_training,
func=activation,
initial_type=initial_type,
trainable=not freeze_head)
if self.use_add_obs:
self.layer_3 = layer.dense_layer(
self.obs_add,
hidden_units,
"DenseLayer3",
norm=False,
is_training=is_training,
func=activation,
initial_type=initial_type)
if self.add_image:
self.layer_4, self.map_variable_scope = resnet(
self.obs_map,
18,
hidden_units,
"Resnet",
is_training=is_training)
else:
print('not add image')
self.layer_4, self.map_variable_scope = self.layer_3, []
#self.layer_4, self.map_variable_scope = ops.simple_resnet_changed(self.obs_map, 18, hidden_units, "Resnet", is_training=is_training)
#self.layer_4, self.map_variable_scope = ops.resnet(self.obs_map, 18, hidden_units, "Resnet", is_training=is_training, reuse=reuse)
if self.weighted_sum_type == 'AttentionWeight':
raise NotImplementedError
elif self.weighted_sum_type == 'AdaptiveWeight':
raise NotImplementedError
elif self.weighted_sum_type == 'AddWeight':
# weighted sum
self.layer_5 = (1. - self.add_weight) * self.layer_2 + self.add_weight / 2. * self.layer_3 + \
self.add_weight / 2. * self.layer_4
elif self.weighted_sum_type == 'Add':
print('self.weighted_sum_type:',
self.weighted_sum_type)
self.layer_5 = self.layer_2 + self.layer_3 + self.layer_4
else:
raise NotImplementedError
else:
self.layer_5 = self.layer_2
probs = layer.output_layer(self.layer_5,
self.act_space_array,
self.add_act_space,
"output",
is_training=is_training,
initial_type=initial_type,
func=tf.nn.softmax)
act = tf.multinomial(tf.log(probs), num_samples=1)
act = tf.reshape(act, shape=[-1])
with tf.variable_scope('value_net'):
layer_1 = layer.dense_layer(self.obs,
hidden_units,
"DenseLayer1",
norm=norm,
is_training=is_training,
func=activation,
initial_type=initial_type,
trainable=not freeze_head)
layer_2 = layer.dense_layer(layer_1,
hidden_units,
"DenseLayer2",
norm=norm,
is_training=is_training,
func=activation,
initial_type=initial_type,
trainable=not freeze_head)
v_preds = layer.dense_layer(layer_2,
1,
"DenseLayer4",
initial_type=initial_type,
is_training=is_training,
func=None)
self.scope = tf.get_variable_scope().name
return probs, act, v_preds
def sep_policy_value_net(self,
name,
hidden_units=64,
activation=tf.nn.relu,
freeze_head=False,
initial_type='original',
norm=True,
is_training=True,
reuse=False):
with tf.variable_scope(name, reuse=reuse):
with tf.variable_scope('policy_net'):
with tf.variable_scope('controller'):
layer_1 = layer.dense_layer(self.obs,
hidden_units,
"DenseLayer1",
norm=norm,
is_training=is_training,
func=activation,
initial_type=initial_type,
trainable=not freeze_head)
self.layer_2 = layer.dense_layer(layer_1,
hidden_units,
"DenseLayer2",
norm=norm,
is_training=is_training,
func=activation,
initial_type=initial_type,
trainable=not freeze_head)
# weighted sum
if self.use_add_obs:
self.layer_3 = layer.dense_layer(
self.obs_add,
hidden_units,
"DenseLayer3",
norm=norm,
is_training=is_training,
func=activation,
initial_type=initial_type)
if self.weight_type == 'AttentionWeight':
self.attention_weight = layer.dense_layer(
self.layer_3,
hidden_units,
"AttentionWeight",
initial_type='original',
func=tf.nn.sigmoid)
#self.attention_weight = tf.nn.sigmoid(self.layer_3)
self.layer_4 = self.layer_2 * self.attention_weight
elif self.weight_type == 'AdaptiveWeight':
self.adaptive_weight = tf.get_variable(
name="AdaptiveWeight",
shape=[hidden_units],
initializer=tf.zeros_initializer(),
trainable=True)
self.layer_4 = (
1 - self.adaptive_weight
) * self.layer_2 + self.adaptive_weight * self.layer_3
elif self.weight_type == 'AddWeight':
self.layer_4 = (
1. - self.add_weight
) * self.layer_2 + self.add_weight * self.layer_3
else:
self.layer_4 = self.layer_2 + self.layer_3
else:
self.layer_4 = self.layer_2
#self.train_probs = layer.dense_layer(layer_2, act_space_array, "output", func=tf.nn.softmax)
probs = layer.output_layer(self.layer_4,
self.act_space_array,
self.add_act_space,
"output",
is_training=is_training,
initial_type=initial_type,
func=tf.nn.softmax)
act = tf.multinomial(tf.log(probs), num_samples=1)
act = tf.reshape(act, shape=[-1])
with tf.variable_scope('value_net'):
layer_1 = layer.dense_layer(self.obs,
hidden_units,
"DenseLayer1",
norm=norm,
is_training=is_training,
func=activation,
initial_type=initial_type,
trainable=not freeze_head)
layer_2 = layer.dense_layer(layer_1,
hidden_units,
"DenseLayer2",
norm=norm,
is_training=is_training,
func=activation,
initial_type=initial_type,
trainable=not freeze_head)
v_preds = layer.dense_layer(layer_2,
1,
"DenseLayer4",
initial_type=initial_type,
is_training=is_training,
func=None)
self.scope = tf.get_variable_scope().name
return probs, act, v_preds
