"""GaussianGRUModel."""
import numpy as np
import tensorflow as tf
from garage.tf.distributions import DiagonalGaussian
from garage.tf.models import Model
from garage.tf.models.gru import gru
from garage.tf.models.parameter import recurrent_parameter
[docs]class GaussianGRUModel(Model):
"""GaussianGRUModel.
Args:
output_dim (int): Output dimension of the model.
hidden_dim (int): Hidden dimension for GRU cell for mean.
name (str): Model name, also the variable scope.
hidden_nonlinearity (callable): Activation function for intermediate
dense layer(s). It should return a tf.Tensor. Set it to
None to maintain a linear activation.
hidden_w_init (callable): Initializer function for the weight
of intermediate dense layer(s). The function should return a
tf.Tensor.
hidden_b_init (callable): Initializer function for the bias
of intermediate dense layer(s). The function should return a
tf.Tensor.
recurrent_nonlinearity (callable): Activation function for recurrent
layers. It should return a tf.Tensor. Set it to None to
maintain a linear activation.
recurrent_w_init (callable): Initializer function for the weight
of recurrent layer(s). The function should return a
tf.Tensor.
output_nonlinearity (callable): Activation function for output dense
layer. It should return a tf.Tensor. Set it to None to
maintain a linear activation.
output_w_init (callable): Initializer function for the weight
of output dense layer(s). The function should return a
tf.Tensor.
output_b_init (callable): Initializer function for the bias
of output dense layer(s). The function should return a
tf.Tensor.
hidden_state_init (callable): Initializer function for the
initial hidden state. The functino should return a tf.Tensor.
hidden_state_init_trainable (bool): Bool for whether the initial
hidden state is trainable.
learn_std (bool): Is std trainable.
init_std (float): Initial value for std.
std_share_network (bool): Boolean for whether mean and std share
the same network.
layer_normalization (bool): Bool for using layer normalization or not.
"""
def __init__(self,
output_dim,
hidden_dim=32,
name=None,
hidden_nonlinearity=tf.nn.tanh,
hidden_w_init=tf.glorot_uniform_initializer(),
hidden_b_init=tf.zeros_initializer(),
recurrent_nonlinearity=tf.nn.sigmoid,
recurrent_w_init=tf.glorot_uniform_initializer(),
output_nonlinearity=None,
output_w_init=tf.glorot_uniform_initializer(),
output_b_init=tf.zeros_initializer(),
hidden_state_init=tf.zeros_initializer(),
hidden_state_init_trainable=False,
learn_std=True,
init_std=1.0,
std_share_network=False,
layer_normalization=False):
super().__init__(name)
self._output_dim = output_dim
self._hidden_dim = hidden_dim
self._hidden_nonlinearity = hidden_nonlinearity
self._hidden_w_init = hidden_w_init
self._hidden_b_init = hidden_b_init
self._recurrent_nonlinearity = recurrent_nonlinearity
self._recurrent_w_init = recurrent_w_init
self._output_nonlinearity = output_nonlinearity
self._output_w_init = output_w_init
self._output_b_init = output_b_init
self._hidden_state_init = hidden_state_init
self._hidden_state_init_trainable = hidden_state_init_trainable
self._layer_normalization = layer_normalization
self._learn_std = learn_std
self._std_share_network = std_share_network
self._init_std_param = np.log(init_std)
self._initialize()
def _initialize(self):
action_dim = self._output_dim
self._mean_std_gru_cell = tf.keras.layers.GRUCell(
units=self._hidden_dim,
activation=self._hidden_nonlinearity,
kernel_initializer=self._hidden_w_init,
bias_initializer=self._hidden_b_init,
recurrent_activation=self._recurrent_nonlinearity,
recurrent_initializer=self._recurrent_w_init,
name='mean_std_gru_layer')
self._mean_gru_cell = tf.keras.layers.GRUCell(
units=self._hidden_dim,
activation=self._hidden_nonlinearity,
kernel_initializer=self._hidden_w_init,
bias_initializer=self._hidden_b_init,
recurrent_activation=self._recurrent_nonlinearity,
recurrent_initializer=self._recurrent_w_init,
name='mean_gru_layer')
self._mean_std_output_nonlinearity_layer = tf.keras.layers.Dense(
units=action_dim * 2,
activation=self._output_nonlinearity,
kernel_initializer=self._output_w_init,
bias_initializer=self._output_b_init,
name='mean_std_output_layer')
self._mean_output_nonlinearity_layer = tf.keras.layers.Dense(
units=action_dim,
activation=self._output_nonlinearity,
kernel_initializer=self._output_w_init,
bias_initializer=self._output_b_init,
name='mean_output_layer')
[docs] def network_output_spec(self):
"""Network output spec."""
return [
'mean', 'step_mean', 'log_std', 'step_log_std', 'step_hidden',
'init_hidden', 'dist'
]
def _build(self, state_input, step_input, hidden_input, name=None):
action_dim = self._output_dim
with tf.compat.v1.variable_scope('dist_params'):
if self._std_share_network:
# mean and std networks share an MLP
(outputs, step_outputs, step_hidden, hidden_init_var) = gru(
name='mean_std_network',
gru_cell=self._mean_std_gru_cell,
all_input_var=state_input,
step_input_var=step_input,
step_hidden_var=hidden_input,
hidden_state_init=self._hidden_state_init,
hidden_state_init_trainable=self.
_hidden_state_init_trainable,
output_nonlinearity_layer=self.
_mean_std_output_nonlinearity_layer)
with tf.compat.v1.variable_scope('mean_network'):
mean_var = outputs[..., :action_dim]
step_mean_var = step_outputs[..., :action_dim]
with tf.compat.v1.variable_scope('log_std_network'):
log_std_var = outputs[..., action_dim:]
step_log_std_var = step_outputs[..., action_dim:]
else:
# separate MLPs for mean and std networks
# mean network
(mean_var, step_mean_var, step_hidden, hidden_init_var) = gru(
name='mean_network',
gru_cell=self._mean_gru_cell,
all_input_var=state_input,
step_input_var=step_input,
step_hidden_var=hidden_input,
hidden_state_init=self._hidden_state_init,
hidden_state_init_trainable=self.
_hidden_state_init_trainable,
output_nonlinearity_layer=self.
_mean_output_nonlinearity_layer)
log_std_var, step_log_std_var = recurrent_parameter(
input_var=state_input,
step_input_var=step_input,
length=action_dim,
initializer=tf.constant_initializer(self._init_std_param),
trainable=self._learn_std,
name='log_std_param')
dist = DiagonalGaussian(self._output_dim)
return (mean_var, step_mean_var, log_std_var, step_log_std_var,
step_hidden, hidden_init_var, dist)
def __getstate__(self):
"""Object.__getstate__."""
new_dict = super().__getstate__()
del new_dict['_mean_std_gru_cell']
del new_dict['_mean_gru_cell']
del new_dict['_mean_std_output_nonlinearity_layer']
del new_dict['_mean_output_nonlinearity_layer']
return new_dict
def __setstate__(self, state):
"""Object.__setstate__."""
super().__setstate__(state)
self._initialize()