def build_graph(self):
"""
Builds computational graph for policy
"""
with tf.variable_scope(self.name):
# build the actual policy network
self.obs_var, self.mean_var = create_mlp(name='mean_network',
output_dim=self.action_dim,
hidden_sizes=self.hidden_sizes,
hidden_nonlinearity=self.hidden_nonlinearity,
output_nonlinearity=self.output_nonlinearity,
input_dim=(None, self.obs_dim,)
)
with tf.variable_scope("log_std_network"):
log_std_var = tf.get_variable(name='log_std_var',
shape=(1, self.action_dim,),
dtype=tf.float32,
initializer=tf.constant_initializer(self.init_log_std),
trainable=self.learn_std
)
self.log_std_var = tf.maximum(log_std_var, self.min_log_std, name='log_std')
# symbolically define sampled action and distribution
self.action_var = self.mean_var + tf.random_normal(shape=tf.shape(self.mean_var)) * tf.exp(log_std_var)
self._dist = DiagonalGaussian(self.action_dim)
# save the policy's trainable variables in dicts
current_scope = tf.get_default_graph().get_name_scope()
trainable_policy_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=current_scope)
self.policy_params = OrderedDict([(remove_scope_from_name(var.name, current_scope), var) for var in trainable_policy_vars])
def build_graph(self):
with tf.variable_scope(self.name):
# build the actual policy network
self.obs_var, self.prob_var = create_mlp(
name='prob_network',
output_dim=self.action_dim,
hidden_sizes=self.hidden_sizes,
hidden_nonlinearity=self.hidden_nonlinearity,
output_nonlinearity=self.output_nonlinearity,
input_dim=(
None,
self.obs_dim,
))
# symbolically define sampled action and distribution
self.action_var = tf.random.categorical(tf.log(self.prob_var), 1)
self._dist = Categorical(self.action_dim)
# save the policy's trainable variables in dicts
current_scope = tf.get_default_graph().get_name_scope()
trainable_policy_vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, scope=current_scope)
self.policy_params = OrderedDict([
(remove_scope_from_name(var.name, current_scope), var)
for var in trainable_policy_vars
])
def _create_placeholders_for_vars(self,
scope,
graph_keys=tf.GraphKeys.
TRAINABLE_VARIABLES):
var_list = tf.get_collection(graph_keys, scope=scope)
placeholders = []
for var in var_list:
var_name = remove_scope_from_name(var.name, scope.split('/')[0])
placeholders.append((var_name,
tf.placeholder(tf.float32,
shape=var.shape,
name="%s_ph" % var_name)))
return OrderedDict(placeholders)
