def distribution(self):
"""Policy distribution.
Returns:
garage.tf.distributions.DiagonalGaussian: Policy distribution.
"""
return RecurrentCategorical(self._action_dim)
def __init__(
self,
env_spec,
name="CategoricalGRUPolicy",
hidden_dim=32,
feature_network=None,
state_include_action=True,
hidden_nonlinearity=tf.tanh,
gru_layer_cls=L.GRULayer,
):
"""
:param env_spec: A spec for the env.
:param hidden_dim: dimension of hidden layer
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:return:
"""
assert isinstance(env_spec.action_space, Discrete)
self._prob_network_name = "prob_network"
with tf.variable_scope(name, "CategoricalGRUPolicy"):
Serializable.quick_init(self, locals())
super(CategoricalGRUPolicy, self).__init__(env_spec)
obs_dim = env_spec.observation_space.flat_dim
action_dim = env_spec.action_space.flat_dim
if state_include_action:
input_dim = obs_dim + action_dim
else:
input_dim = obs_dim
l_input = L.InputLayer(shape=(None, None, input_dim), name="input")
if feature_network is None:
feature_dim = input_dim
l_flat_feature = None
l_feature = l_input
else:
feature_dim = feature_network.output_layer.output_shape[-1]
l_flat_feature = feature_network.output_layer
l_feature = L.OpLayer(
l_flat_feature,
extras=[l_input],
name="reshape_feature",
op=lambda flat_feature, input: tf.reshape(
flat_feature,
tf.stack([
tf.shape(input)[0],
tf.shape(input)[1], feature_dim
])),
shape_op=lambda _, input_shape:
(input_shape[0], input_shape[1], feature_dim))
prob_network = GRUNetwork(input_shape=(feature_dim, ),
input_layer=l_feature,
output_dim=env_spec.action_space.n,
hidden_dim=hidden_dim,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=tf.nn.softmax,
gru_layer_cls=gru_layer_cls,
name=self._prob_network_name)
self.prob_network = prob_network
self.feature_network = feature_network
self.l_input = l_input
self.state_include_action = state_include_action
flat_input_var = tf.placeholder(dtype=tf.float32,
shape=(None, input_dim),
name="flat_input")
if feature_network is None:
feature_var = flat_input_var
else:
with tf.name_scope("feature_network", values=[flat_input_var]):
feature_var = L.get_output(
l_flat_feature,
{feature_network.input_layer: flat_input_var})
with tf.name_scope(self._prob_network_name, values=[feature_var]):
out_prob_step, out_prob_hidden = L.get_output(
[
prob_network.step_output_layer,
prob_network.step_hidden_layer
], {prob_network.step_input_layer: feature_var})
out_prob_step = tf.identity(out_prob_step, "prob_step_output")
out_prob_hidden = tf.identity(out_prob_hidden,
"prob_step_hidden")
self.f_step_prob = tensor_utils.compile_function(
[flat_input_var, prob_network.step_prev_state_layer.input_var],
[out_prob_step, out_prob_hidden])
self.input_dim = input_dim
self.action_dim = action_dim
self.hidden_dim = hidden_dim
self.name = name
self.prev_actions = None
self.prev_hiddens = None
self.dist = RecurrentCategorical(env_spec.action_space.n)
out_layers = [prob_network.output_layer]
if feature_network is not None:
out_layers.append(feature_network.output_layer)
LayersPowered.__init__(self, out_layers)
def distribution(self):
"""Policy distribution."""
return RecurrentCategorical(self._action_dim)
def __init__(self,
env_spec,
name='CategoricalLSTMPolicy',
hidden_dim=32,
hidden_nonlinearity=tf.nn.tanh,
recurrent_nonlinearity=tf.nn.sigmoid,
recurrent_w_x_init=L.XavierUniformInitializer(),
recurrent_w_h_init=L.OrthogonalInitializer(),
output_nonlinearity=tf.nn.softmax,
output_w_init=L.XavierUniformInitializer(),
feature_network=None,
prob_network=None,
state_include_action=True,
forget_bias=1.0,
use_peepholes=False,
lstm_layer_cls=L.LSTMLayer):
"""
:param env_spec: A spec for the env.
:param hidden_dim: dimension of hidden layer
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:return:
"""
assert isinstance(env_spec.action_space, Discrete)
self._prob_network_name = 'prob_network'
with tf.variable_scope(name, 'CategoricalLSTMPolicy'):
Serializable.quick_init(self, locals())
super(CategoricalLSTMPolicy, self).__init__(env_spec)
obs_dim = env_spec.observation_space.flat_dim
action_dim = env_spec.action_space.flat_dim
if state_include_action:
input_dim = obs_dim + action_dim
else:
input_dim = obs_dim
l_input = L.InputLayer(shape=(None, None, input_dim), name='input')
if feature_network is None:
feature_dim = input_dim
l_flat_feature = None
l_feature = l_input
else:
feature_dim = feature_network.output_layer.output_shape[-1]
l_flat_feature = feature_network.output_layer
l_feature = L.OpLayer(
l_flat_feature,
extras=[l_input],
name='reshape_feature',
op=lambda flat_feature, input: tf.reshape(
flat_feature,
tf.stack([
tf.shape(input)[0],
tf.shape(input)[1], feature_dim
])),
shape_op=lambda _, input_shape:
(input_shape[0], input_shape[1], feature_dim))
if prob_network is None:
prob_network = LSTMNetwork(
input_shape=(feature_dim, ),
input_layer=l_feature,
output_dim=env_spec.action_space.n,
hidden_dim=hidden_dim,
hidden_nonlinearity=hidden_nonlinearity,
recurrent_nonlinearity=recurrent_nonlinearity,
recurrent_w_x_init=recurrent_w_x_init,
recurrent_w_h_init=recurrent_w_h_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
forget_bias=forget_bias,
use_peepholes=use_peepholes,
lstm_layer_cls=lstm_layer_cls,
name=self._prob_network_name)
self.prob_network = prob_network
self.feature_network = feature_network
self.l_input = l_input
self.state_include_action = state_include_action
flat_input_var = tf.placeholder(dtype=tf.float32,
shape=(None, input_dim),
name='flat_input')
if feature_network is None:
feature_var = flat_input_var
else:
with tf.name_scope('feature_network', values=[flat_input_var]):
feature_var = L.get_output(
l_flat_feature,
{feature_network.input_layer: flat_input_var})
with tf.name_scope(self._prob_network_name, values=[feature_var]):
out_prob_step, out_prob_hidden, out_step_cell = L.get_output(
[
prob_network.step_output_layer,
prob_network.step_hidden_layer,
prob_network.step_cell_layer
], {prob_network.step_input_layer: feature_var})
self.f_step_prob = tensor_utils.compile_function([
flat_input_var,
prob_network.step_prev_state_layer.input_var,
], [out_prob_step, out_prob_hidden, out_step_cell])
self.input_dim = input_dim
self.action_dim = action_dim
self.hidden_dim = hidden_dim
self.name = name
self.prev_actions = None
self.prev_hiddens = None
self.prev_cells = None
self.dist = RecurrentCategorical(env_spec.action_space.n)
out_layers = [prob_network.output_layer]
if feature_network is not None:
out_layers.append(feature_network.output_layer)
LayersPowered.__init__(self, out_layers)
