def __init__(self,
input_shape,
output_dim,
name="GaussianMLPRegressor",
mean_network=None,
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.tanh,
optimizer=None,
optimizer_args=None,
use_trust_region=True,
max_kl_step=0.01,
learn_std=True,
init_std=1.0,
adaptive_std=False,
std_share_network=False,
std_hidden_sizes=(32, 32),
std_nonlinearity=None,
normalize_inputs=True,
normalize_outputs=True,
subsample_factor=1.0):
"""
:param input_shape: Shape of the input data.
:param output_dim: Dimension of output.
:param hidden_sizes: Number of hidden units of each layer of the mean
network.
:param hidden_nonlinearity: Non-linearity used for each layer of the
mean network.
:param optimizer: Optimizer for minimizing the negative log-likelihood.
:param use_trust_region: Whether to use trust region constraint.
:param max_kl_step: KL divergence constraint for each iteration
:param learn_std: Whether to learn the standard deviations. Only
effective if adaptive_std is False. If adaptive_std is True, this
parameter is ignored, and the weights for the std network are always
earned.
:param adaptive_std: Whether to make the std a function of the states.
:param std_share_network: Whether to use the same network as the mean.
:param std_hidden_sizes: Number of hidden units of each layer of the
std network. Only used if `std_share_network` is False. It defaults to
the same architecture as the mean.
:param std_nonlinearity: Non-linearity used for each layer of the std
network. Only used if `std_share_network` is False. It defaults to the
same non-linearity as the mean.
"""
Parameterized.__init__(self)
Serializable.quick_init(self, locals())
self._mean_network_name = "mean_network"
self._std_network_name = "std_network"
with tf.variable_scope(name):
if optimizer_args is None:
optimizer_args = dict()
if optimizer is None:
if use_trust_region:
optimizer = PenaltyLbfgsOptimizer(**optimizer_args)
else:
optimizer = LbfgsOptimizer(**optimizer_args)
else:
optimizer = optimizer(**optimizer_args)
self._optimizer = optimizer
self._subsample_factor = subsample_factor
if mean_network is None:
if std_share_network:
mean_network = MLP(
name="mean_network",
input_shape=input_shape,
output_dim=2 * output_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=None,
)
l_mean = L.SliceLayer(
mean_network.output_layer,
slice(output_dim),
name="mean_slice",
)
else:
mean_network = MLP(
name="mean_network",
input_shape=input_shape,
output_dim=output_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=None,
)
l_mean = mean_network.output_layer
if adaptive_std:
l_log_std = MLP(
name="log_std_network",
input_shape=input_shape,
input_var=mean_network.input_layer.input_var,
output_dim=output_dim,
hidden_sizes=std_hidden_sizes,
hidden_nonlinearity=std_nonlinearity,
output_nonlinearity=None,
).output_layer
elif std_share_network:
l_log_std = L.SliceLayer(
mean_network.output_layer,
slice(output_dim, 2 * output_dim),
name="log_std_slice",
)
else:
l_log_std = L.ParamLayer(
mean_network.input_layer,
num_units=output_dim,
param=tf.constant_initializer(np.log(init_std)),
name="output_log_std",
trainable=learn_std,
)
LayersPowered.__init__(self, [l_mean, l_log_std])
xs_var = mean_network.input_layer.input_var
ys_var = tf.placeholder(dtype=tf.float32,
name="ys",
shape=(None, output_dim))
old_means_var = tf.placeholder(dtype=tf.float32,
name="ys",
shape=(None, output_dim))
old_log_stds_var = tf.placeholder(dtype=tf.float32,
name="old_log_stds",
shape=(None, output_dim))
x_mean_var = tf.Variable(
np.zeros((1, ) + input_shape, dtype=np.float32),
name="x_mean",
)
x_std_var = tf.Variable(
np.ones((1, ) + input_shape, dtype=np.float32),
name="x_std",
)
y_mean_var = tf.Variable(
np.zeros((1, output_dim), dtype=np.float32),
name="y_mean",
)
y_std_var = tf.Variable(
np.ones((1, output_dim), dtype=np.float32),
name="y_std",
)
normalized_xs_var = (xs_var - x_mean_var) / x_std_var
normalized_ys_var = (ys_var - y_mean_var) / y_std_var
with tf.name_scope(self._mean_network_name,
values=[normalized_xs_var]):
normalized_means_var = L.get_output(
l_mean, {mean_network.input_layer: normalized_xs_var})
with tf.name_scope(self._std_network_name,
values=[normalized_xs_var]):
normalized_log_stds_var = L.get_output(
l_log_std, {mean_network.input_layer: normalized_xs_var})
means_var = normalized_means_var * y_std_var + y_mean_var
log_stds_var = normalized_log_stds_var + tf.log(y_std_var)
normalized_old_means_var = (old_means_var - y_mean_var) / y_std_var
normalized_old_log_stds_var = old_log_stds_var - tf.log(y_std_var)
dist = self._dist = DiagonalGaussian(output_dim)
normalized_dist_info_vars = dict(mean=normalized_means_var,
log_std=normalized_log_stds_var)
mean_kl = tf.reduce_mean(
dist.kl_sym(
dict(mean=normalized_old_means_var,
log_std=normalized_old_log_stds_var),
normalized_dist_info_vars,
))
loss = -tf.reduce_mean(
dist.log_likelihood_sym(normalized_ys_var,
normalized_dist_info_vars))
self._f_predict = tensor_utils.compile_function([xs_var],
means_var)
self._f_pdists = tensor_utils.compile_function(
[xs_var], [means_var, log_stds_var])
self._l_mean = l_mean
self._l_log_std = l_log_std
optimizer_args = dict(
loss=loss,
target=self,
network_outputs=[
normalized_means_var, normalized_log_stds_var
],
)
if use_trust_region:
optimizer_args["leq_constraint"] = (mean_kl, max_kl_step)
optimizer_args["inputs"] = [
xs_var, ys_var, old_means_var, old_log_stds_var
]
else:
optimizer_args["inputs"] = [xs_var, ys_var]
self._optimizer.update_opt(**optimizer_args)
self._use_trust_region = use_trust_region
self._name = name
self._normalize_inputs = normalize_inputs
self._normalize_outputs = normalize_outputs
self._mean_network = mean_network
self._x_mean_var = x_mean_var
self._x_std_var = x_std_var
self._y_mean_var = y_mean_var
self._y_std_var = y_std_var
# Optionally create assign operations for normalization
if self._normalize_inputs:
self._x_mean_var_ph = tf.placeholder(
shape=(1, ) + input_shape,
dtype=tf.float32,
)
self._x_std_var_ph = tf.placeholder(
shape=(1, ) + input_shape,
dtype=tf.float32,
)
self._assign_x_mean = tf.assign(self._x_mean_var,
self._x_mean_var_ph)
self._assign_x_std = tf.assign(self._x_std_var,
self._x_std_var_ph)
if self._normalize_outputs:
self._y_mean_var_ph = tf.placeholder(
shape=(1, output_dim),
dtype=tf.float32,
)
self._y_std_var_ph = tf.placeholder(
shape=(1, output_dim),
dtype=tf.float32,
)
self._assign_y_mean = tf.assign(self._y_mean_var,
self._y_mean_var_ph)
self._assign_y_std = tf.assign(self._y_std_var,
self._y_std_var_ph)
def __init__(self,
env_spec,
name=None,
hidden_sizes=(32, 32),
learn_std=True,
init_std=1.0,
adaptive_std=False,
std_share_network=False,
std_hidden_sizes=(32, 32),
min_std=1e-6,
std_hidden_nonlinearity=tf.nn.tanh,
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
mean_network=None,
std_network=None,
std_parametrization='exp'):
"""
:param env_spec:
:param hidden_sizes: list of sizes for the fully-connected hidden
layers
:param learn_std: Is std trainable
:param init_std: Initial std
:param adaptive_std:
:param std_share_network:
:param std_hidden_sizes: list of sizes for the fully-connected layers
for std
:param min_std: whether to make sure that the std is at least some
threshold value, to avoid numerical issues
:param std_hidden_nonlinearity:
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:param output_nonlinearity: nonlinearity for the output layer
:param mean_network: custom network for the output mean
:param std_network: custom network for the output log std
:param std_parametrization: how the std should be parametrized. There
are a few options:
- exp: the logarithm of the std will be stored, and applied a
exponential transformation
- softplus: the std will be computed as log(1+exp(x))
:return:
"""
Serializable.quick_init(self, locals())
assert isinstance(env_spec.action_space, Box)
self.name = name
self._mean_network_name = "mean_network"
self._std_network_name = "std_network"
with tf.variable_scope(name, "GaussianMLPPolicy"):
obs_dim = env_spec.observation_space.flat_dim
action_dim = env_spec.action_space.flat_dim
# create network
if mean_network is None:
if std_share_network:
if std_parametrization == "exp":
init_std_param = np.log(init_std)
elif std_parametrization == "softplus":
init_std_param = np.log(np.exp(init_std) - 1)
else:
raise NotImplementedError
init_b = tf.constant_initializer(init_std_param)
with tf.variable_scope(self._mean_network_name):
mean_network = MLP(
name="mlp",
input_shape=(obs_dim, ),
output_dim=2 * action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
output_b_init=init_b,
)
l_mean = L.SliceLayer(
mean_network.output_layer,
slice(action_dim),
name="mean_slice",
)
else:
mean_network = MLP(
name=self._mean_network_name,
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
)
l_mean = mean_network.output_layer
self._mean_network = mean_network
obs_var = mean_network.input_layer.input_var
if std_network is not None:
l_std_param = std_network.output_layer
else:
if adaptive_std:
std_network = MLP(
name=self._std_network_name,
input_shape=(obs_dim, ),
input_layer=mean_network.input_layer,
output_dim=action_dim,
hidden_sizes=std_hidden_sizes,
hidden_nonlinearity=std_hidden_nonlinearity,
output_nonlinearity=None,
)
l_std_param = std_network.output_layer
elif std_share_network:
with tf.variable_scope(self._std_network_name):
l_std_param = L.SliceLayer(
mean_network.output_layer,
slice(action_dim, 2 * action_dim),
name="std_slice",
)
else:
if std_parametrization == 'exp':
init_std_param = np.log(init_std)
elif std_parametrization == 'softplus':
init_std_param = np.log(np.exp(init_std) - 1)
else:
raise NotImplementedError
with tf.variable_scope(self._std_network_name):
l_std_param = L.ParamLayer(
mean_network.input_layer,
num_units=action_dim,
param=tf.constant_initializer(init_std_param),
name="output_std_param",
trainable=learn_std,
)
self.std_parametrization = std_parametrization
if std_parametrization == 'exp':
min_std_param = np.log(min_std)
elif std_parametrization == 'softplus':
min_std_param = np.log(np.exp(min_std) - 1)
else:
raise NotImplementedError
self.min_std_param = min_std_param
# mean_var, log_std_var = L.get_output([l_mean, l_std_param])
#
# if self.min_std_param is not None:
# log_std_var = tf.maximum(log_std_var, np.log(min_std))
#
# self._mean_var, self._log_std_var = mean_var, log_std_var
self._l_mean = l_mean
self._l_std_param = l_std_param
self._dist = DiagonalGaussian(action_dim)
LayersPowered.__init__(self, [l_mean, l_std_param])
super(GaussianMLPPolicy, self).__init__(env_spec)
dist_info_sym = self.dist_info_sym(
mean_network.input_layer.input_var, dict())
mean_var = tf.identity(dist_info_sym["mean"], name="mean")
log_std_var = tf.identity(dist_info_sym["log_std"],
name="standard_dev")
self._f_dist = tensor_utils.compile_function(
inputs=[obs_var],
outputs=[mean_var, log_std_var],
)
def __init__(
self,
env_spec,
name="GaussianGRUPolicy",
hidden_dim=32,
feature_network=None,
state_include_action=True,
hidden_nonlinearity=tf.tanh,
gru_layer_cls=L.GRULayer,
learn_std=True,
init_std=1.0,
output_nonlinearity=None,
std_share_network=False,
):
"""
: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, Box)
self._mean_network_name = "mean_network"
self._std_network_name = "std_network"
with tf.variable_scope(name, "GaussianGRUPolicy"):
Serializable.quick_init(self, locals())
super(GaussianGRUPolicy, 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 std_share_network:
mean_network = GRUNetwork(
input_shape=(feature_dim, ),
input_layer=l_feature,
output_dim=2 * action_dim,
hidden_dim=hidden_dim,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
gru_layer_cls=gru_layer_cls,
name="gru_mean_network")
l_mean = L.SliceLayer(mean_network.output_layer,
slice(action_dim),
name="mean_slice")
l_step_mean = L.SliceLayer(mean_network.step_output_layer,
slice(action_dim),
name="step_mean_slice")
l_log_std = L.SliceLayer(mean_network.output_layer,
slice(action_dim, 2 * action_dim),
name="log_std_slice")
l_step_log_std = L.SliceLayer(mean_network.step_output_layer,
slice(action_dim,
2 * action_dim),
name="step_log_std_slice")
else:
mean_network = GRUNetwork(
input_shape=(feature_dim, ),
input_layer=l_feature,
output_dim=action_dim,
hidden_dim=hidden_dim,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
gru_layer_cls=gru_layer_cls,
name="gru_mean_network")
l_mean = mean_network.output_layer
l_step_mean = mean_network.step_output_layer
l_log_std = L.ParamLayer(
mean_network.input_layer,
num_units=action_dim,
param=tf.constant_initializer(np.log(init_std)),
name="output_log_std",
trainable=learn_std,
)
l_step_log_std = L.ParamLayer(
mean_network.step_input_layer,
num_units=action_dim,
param=l_log_std.param,
name="step_output_log_std",
trainable=learn_std,
)
self.mean_network = mean_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:
feature_var = L.get_output(
l_flat_feature,
{feature_network.input_layer: flat_input_var})
with tf.name_scope(self._mean_network_name):
out_step_mean, out_step_hidden_mean = L.get_output(
[l_step_mean, mean_network.step_hidden_layer],
{mean_network.step_input_layer: feature_var})
out_step_mean = tf.identity(out_step_mean, "step_mean")
out_step_hidden_mean = tf.identity(out_step_hidden_mean,
"step_hidden_mean")
with tf.name_scope(self._std_network_name):
out_step_log_std = L.get_output(
l_step_log_std,
{mean_network.step_input_layer: feature_var})
out_step_log_std = tf.identity(out_step_log_std,
"step_log_std")
self.f_step_mean_std = tensor_utils.compile_function([
flat_input_var,
mean_network.step_prev_state_layer.input_var,
], [out_step_mean, out_step_log_std, out_step_hidden_mean])
self.l_mean = l_mean
self.l_log_std = l_log_std
self.input_dim = input_dim
self.action_dim = action_dim
self.hidden_dim = hidden_dim
self.prev_actions = None
self.prev_hiddens = None
self.dist = RecurrentDiagonalGaussian(action_dim)
self.name = name
out_layers = [l_mean, l_log_std, l_step_log_std]
if feature_network is not None:
out_layers.append(feature_network.output_layer)
LayersPowered.__init__(self, out_layers)
def __init__(self,
input_shape,
extra_input_shape,
output_dim,
hidden_sizes,
conv_filters,
conv_filter_sizes,
conv_strides,
conv_pads,
name=None,
extra_hidden_sizes=None,
hidden_w_init=ly.XavierUniformInitializer(),
hidden_b_init=tf.zeros_initializer(),
output_w_init=ly.XavierUniformInitializer(),
output_b_init=tf.zeros_initializer(),
hidden_nonlinearity=tf.nn.relu,
output_nonlinearity=None,
input_var=None,
input_layer=None):
Serializable.quick_init(self, locals())
if extra_hidden_sizes is None:
extra_hidden_sizes = []
with tf.variable_scope(name, 'ConvMergeNetwork'):
input_flat_dim = np.prod(input_shape)
extra_input_flat_dim = np.prod(extra_input_shape)
total_input_flat_dim = input_flat_dim + extra_input_flat_dim
if input_layer is None:
l_in = ly.InputLayer(shape=(None, total_input_flat_dim),
input_var=input_var,
name='input')
else:
l_in = input_layer
l_conv_in = ly.reshape(ly.SliceLayer(l_in,
indices=slice(input_flat_dim),
name='conv_slice'),
([0], ) + input_shape,
name='conv_reshaped')
l_extra_in = ly.reshape(ly.SliceLayer(l_in,
indices=slice(
input_flat_dim, None),
name='extra_slice'),
([0], ) + extra_input_shape,
name='extra_reshaped')
l_conv_hid = l_conv_in
for idx, conv_filter, filter_size, stride, pad in zip(
range(len(conv_filters)),
conv_filters,
conv_filter_sizes,
conv_strides,
conv_pads,
):
l_conv_hid = ly.Conv2DLayer(
l_conv_hid,
num_filters=conv_filter,
filter_size=filter_size,
stride=(stride, stride),
pad=pad,
nonlinearity=hidden_nonlinearity,
name='conv_hidden_%d' % idx,
)
l_extra_hid = l_extra_in
for idx, hidden_size in enumerate(extra_hidden_sizes):
l_extra_hid = ly.DenseLayer(
l_extra_hid,
num_units=hidden_size,
nonlinearity=hidden_nonlinearity,
name='extra_hidden_%d' % idx,
w=hidden_w_init,
b=hidden_b_init,
)
l_joint_hid = ly.concat(
[ly.flatten(l_conv_hid, name='conv_hidden_flat'), l_extra_hid],
name='joint_hidden')
for idx, hidden_size in enumerate(hidden_sizes):
l_joint_hid = ly.DenseLayer(
l_joint_hid,
num_units=hidden_size,
nonlinearity=hidden_nonlinearity,
name='joint_hidden_%d' % idx,
w=hidden_w_init,
b=hidden_b_init,
)
l_out = ly.DenseLayer(
l_joint_hid,
num_units=output_dim,
nonlinearity=output_nonlinearity,
name='output',
w=output_w_init,
b=output_b_init,
)
self._l_in = l_in
self._l_out = l_out
LayersPowered.__init__(self, [l_out], input_layers=[l_in])
def __init__(self,
input_shape,
output_dim,
hidden_dim,
name=None,
hidden_nonlinearity=tf.nn.relu,
output_w_init=ly.XavierUniformInitializer(),
recurrent_nonlinearity=tf.nn.sigmoid,
recurrent_w_x_init=ly.XavierUniformInitializer(),
recurrent_w_h_init=ly.OrthogonalInitializer(),
lstm_layer_cls=ly.LSTMLayer,
output_nonlinearity=None,
input_var=None,
input_layer=None,
forget_bias=1.0,
use_peepholes=False,
layer_args=None):
with tf.variable_scope(name, 'LSTMNetwork'):
if input_layer is None:
l_in = ly.InputLayer(shape=(None, None) + input_shape,
input_var=input_var,
name='input')
else:
l_in = input_layer
l_step_input = ly.InputLayer(shape=(None, ) + input_shape,
name='step_input')
# contains previous hidden and cell state
l_step_prev_state = ly.InputLayer(shape=(None, hidden_dim * 2),
name='step_prev_state')
if layer_args is None:
layer_args = dict()
l_lstm = lstm_layer_cls(l_in,
num_units=hidden_dim,
hidden_nonlinearity=hidden_nonlinearity,
gate_nonlinearity=recurrent_nonlinearity,
hidden_init_trainable=False,
name='lstm_layer',
forget_bias=forget_bias,
cell_init_trainable=False,
w_x_init=recurrent_w_x_init,
w_h_init=recurrent_w_h_init,
use_peepholes=use_peepholes,
**layer_args)
l_lstm_flat = ly.ReshapeLayer(l_lstm,
shape=(-1, hidden_dim),
name='lstm_flat')
l_output_flat = ly.DenseLayer(l_lstm_flat,
num_units=output_dim,
nonlinearity=output_nonlinearity,
w=output_w_init,
name='output_flat')
l_output = ly.OpLayer(
l_output_flat,
op=lambda flat_output, l_input: tf.reshape(
flat_output,
tf.stack(
(tf.shape(l_input)[0], tf.shape(l_input)[1], -1))),
shape_op=lambda flat_output_shape, l_input_shape:
(l_input_shape[0], l_input_shape[1], flat_output_shape[-1]),
extras=[l_in],
name='output')
l_step_state = l_lstm.get_step_layer(l_step_input,
l_step_prev_state,
name='step_state')
l_step_hidden = ly.SliceLayer(l_step_state,
indices=slice(hidden_dim),
name='step_hidden')
l_step_cell = ly.SliceLayer(l_step_state,
indices=slice(hidden_dim, None),
name='step_cell')
l_step_output = ly.DenseLayer(l_step_hidden,
num_units=output_dim,
nonlinearity=output_nonlinearity,
w=l_output_flat.w,
b=l_output_flat.b,
name='step_output')
self._l_in = l_in
self._hid_init_param = l_lstm.h0
self._cell_init_param = l_lstm.c0
self._l_lstm = l_lstm
self._l_out = l_output
self._l_step_input = l_step_input
self._l_step_prev_state = l_step_prev_state
self._l_step_hidden = l_step_hidden
self._l_step_cell = l_step_cell
self._l_step_state = l_step_state
self._l_step_output = l_step_output
self._hidden_dim = hidden_dim
def __init__(self,
input_shape,
output_dim,
conv_filters,
conv_filter_sizes,
conv_strides,
conv_pads,
hidden_sizes,
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
name='GaussianConvRegressor',
mean_network=None,
learn_std=True,
init_std=1.0,
adaptive_std=False,
std_share_network=False,
std_conv_filters=[],
std_conv_filter_sizes=[],
std_conv_strides=[],
std_conv_pads=[],
std_hidden_sizes=[],
std_hidden_nonlinearity=None,
std_output_nonlinearity=None,
normalize_inputs=True,
normalize_outputs=True,
subsample_factor=1.,
optimizer=None,
optimizer_args=dict(),
use_trust_region=True,
max_kl_step=0.01):
Parameterized.__init__(self)
Serializable.quick_init(self, locals())
self._mean_network_name = 'mean_network'
self._std_network_name = 'std_network'
with tf.compat.v1.variable_scope(name):
if optimizer is None:
if use_trust_region:
optimizer = PenaltyLbfgsOptimizer(**optimizer_args)
else:
optimizer = LbfgsOptimizer(**optimizer_args)
else:
optimizer = optimizer(**optimizer_args)
self._optimizer = optimizer
self._subsample_factor = subsample_factor
if mean_network is None:
if std_share_network:
b = np.concatenate(
[
np.zeros(output_dim),
np.full(output_dim, np.log(init_std))
],
axis=0) # yapf: disable
b = tf.constant_initializer(b)
mean_network = ConvNetwork(
name=self._mean_network_name,
input_shape=input_shape,
output_dim=2 * output_dim,
conv_filters=conv_filters,
conv_filter_sizes=conv_filter_sizes,
conv_strides=conv_strides,
conv_pads=conv_pads,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
output_b_init=b)
l_mean = layers.SliceLayer(
mean_network.output_layer,
slice(output_dim),
name='mean_slice',
)
else:
mean_network = ConvNetwork(
name=self._mean_network_name,
input_shape=input_shape,
output_dim=output_dim,
conv_filters=conv_filters,
conv_filter_sizes=conv_filter_sizes,
conv_strides=conv_strides,
conv_pads=conv_pads,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity)
l_mean = mean_network.output_layer
if adaptive_std:
l_log_std = ConvNetwork(
name=self._std_network_name,
input_shape=input_shape,
output_dim=output_dim,
conv_filters=std_conv_filters,
conv_filter_sizes=std_conv_filter_sizes,
conv_strides=std_conv_strides,
conv_pads=std_conv_pads,
hidden_sizes=std_hidden_sizes,
hidden_nonlinearity=std_hidden_nonlinearity,
output_nonlinearity=std_output_nonlinearity,
output_b_init=tf.constant_initializer(np.log(init_std)),
).output_layer
elif std_share_network:
l_log_std = layers.SliceLayer(
mean_network.output_layer,
slice(output_dim, 2 * output_dim),
name='log_std_slice',
)
else:
l_log_std = layers.ParamLayer(
mean_network.input_layer,
num_units=output_dim,
param=tf.constant_initializer(np.log(init_std)),
trainable=learn_std,
name=self._std_network_name,
)
LayersPowered.__init__(self, [l_mean, l_log_std])
xs_var = mean_network.input_layer.input_var
ys_var = tf.compat.v1.placeholder(
dtype=tf.float32, name='ys', shape=(None, output_dim))
old_means_var = tf.compat.v1.placeholder(
dtype=tf.float32, name='ys', shape=(None, output_dim))
old_log_stds_var = tf.compat.v1.placeholder(
dtype=tf.float32,
name='old_log_stds',
shape=(None, output_dim))
x_mean_var = tf.Variable(
np.zeros((1, np.prod(input_shape)), dtype=np.float32),
name='x_mean',
)
x_std_var = tf.Variable(
np.ones((1, np.prod(input_shape)), dtype=np.float32),
name='x_std',
)
y_mean_var = tf.Variable(
np.zeros((1, output_dim), dtype=np.float32),
name='y_mean',
)
y_std_var = tf.Variable(
np.ones((1, output_dim), dtype=np.float32),
name='y_std',
)
normalized_xs_var = (xs_var - x_mean_var) / x_std_var
normalized_ys_var = (ys_var - y_mean_var) / y_std_var
with tf.name_scope(
self._mean_network_name, values=[normalized_xs_var]):
normalized_means_var = layers.get_output(
l_mean, {mean_network.input_layer: normalized_xs_var})
with tf.name_scope(
self._std_network_name, values=[normalized_xs_var]):
normalized_log_stds_var = layers.get_output(
l_log_std, {mean_network.input_layer: normalized_xs_var})
means_var = normalized_means_var * y_std_var + y_mean_var
log_stds_var = normalized_log_stds_var + tf.math.log(y_std_var)
normalized_old_means_var = (old_means_var - y_mean_var) / y_std_var
normalized_old_log_stds_var = (
old_log_stds_var - tf.math.log(y_std_var))
dist = self._dist = DiagonalGaussian(output_dim)
normalized_dist_info_vars = dict(
mean=normalized_means_var, log_std=normalized_log_stds_var)
mean_kl = tf.reduce_mean(
dist.kl_sym(
dict(
mean=normalized_old_means_var,
log_std=normalized_old_log_stds_var),
normalized_dist_info_vars,
))
loss = -tf.reduce_mean(
dist.log_likelihood_sym(normalized_ys_var,
normalized_dist_info_vars))
self._f_predict = tensor_utils.compile_function([xs_var],
means_var)
self._f_pdists = tensor_utils.compile_function(
[xs_var], [means_var, log_stds_var])
self._l_mean = l_mean
self._l_log_std = l_log_std
optimizer_args = dict(
loss=loss,
target=self,
network_outputs=[
normalized_means_var, normalized_log_stds_var
],
)
if use_trust_region:
optimizer_args['leq_constraint'] = (mean_kl, max_kl_step)
optimizer_args['inputs'] = [
xs_var, ys_var, old_means_var, old_log_stds_var
]
else:
optimizer_args['inputs'] = [xs_var, ys_var]
self._optimizer.update_opt(**optimizer_args)
self._use_trust_region = use_trust_region
self._name = name
self._normalize_inputs = normalize_inputs
self._normalize_outputs = normalize_outputs
self._mean_network = mean_network
self._x_mean_var = x_mean_var
self._x_std_var = x_std_var
self._y_mean_var = y_mean_var
self._y_std_var = y_std_var
def __init__(
self,
env_spec,
name='GaussianLSTMPolicy',
hidden_dim=32,
hidden_nonlinearity=tf.tanh,
recurrent_nonlinearity=tf.nn.sigmoid,
recurrent_w_x_init=L.XavierUniformInitializer(),
recurrent_w_h_init=L.OrthogonalInitializer(),
output_nonlinearity=None,
output_w_init=L.XavierUniformInitializer(),
feature_network=None,
state_include_action=True,
learn_std=True,
init_std=1.0,
lstm_layer_cls=L.LSTMLayer,
use_peepholes=False,
std_share_network=False,
):
"""
: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, akro.Box)
self._mean_network_name = 'mean_network'
self._std_network_name = 'std_network'
with tf.variable_scope(name, 'GaussianLSTMPolicy'):
Serializable.quick_init(self, locals())
super(GaussianLSTMPolicy, 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 std_share_network:
mean_network = LSTMNetwork(
input_shape=(feature_dim, ),
input_layer=l_feature,
output_dim=2 * action_dim,
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,
lstm_layer_cls=lstm_layer_cls,
name='lstm_mean_network',
use_peepholes=use_peepholes,
)
l_mean = L.SliceLayer(
mean_network.output_layer,
slice(action_dim),
name='mean_slice',
)
l_step_mean = L.SliceLayer(
mean_network.step_output_layer,
slice(action_dim),
name='step_mean_slice',
)
l_log_std = L.SliceLayer(
mean_network.output_layer,
slice(action_dim, 2 * action_dim),
name='log_std_slice',
)
l_step_log_std = L.SliceLayer(
mean_network.step_output_layer,
slice(action_dim, 2 * action_dim),
name='step_log_std_slice',
)
else:
mean_network = LSTMNetwork(
input_shape=(feature_dim, ),
input_layer=l_feature,
output_dim=action_dim,
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,
lstm_layer_cls=lstm_layer_cls,
name='lstm_mean_network',
use_peepholes=use_peepholes,
)
l_mean = mean_network.output_layer
l_step_mean = mean_network.step_output_layer
l_log_std = L.ParamLayer(
mean_network.input_layer,
num_units=action_dim,
param=tf.constant_initializer(np.log(init_std)),
name='output_log_std',
trainable=learn_std,
)
l_step_log_std = L.ParamLayer(
mean_network.step_input_layer,
num_units=action_dim,
param=l_log_std.param,
name='step_output_log_std',
trainable=learn_std,
)
self.mean_network = mean_network
self.feature_network = feature_network
self.l_input = l_input
self.state_include_action = state_include_action
self.name = name
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:
feature_var = L.get_output(
l_flat_feature,
{feature_network.input_layer: flat_input_var})
with tf.name_scope(self._mean_network_name, values=[feature_var]):
(out_step_mean, out_step_hidden, out_mean_cell) = L.get_output(
[
l_step_mean, mean_network.step_hidden_layer,
mean_network.step_cell_layer
], {mean_network.step_input_layer: feature_var})
out_step_mean = tf.identity(out_step_mean, 'step_mean')
out_step_hidden = tf.identity(out_step_hidden, 'step_hidden')
out_mean_cell = tf.identity(out_mean_cell, 'mean_cell')
with tf.name_scope(self._std_network_name, values=[feature_var]):
out_step_log_std = L.get_output(
l_step_log_std,
{mean_network.step_input_layer: feature_var})
out_step_log_std = tf.identity(out_step_log_std,
'step_log_std')
self.f_step_mean_std = tensor_utils.compile_function([
flat_input_var,
mean_network.step_prev_state_layer.input_var,
], [
out_step_mean, out_step_log_std, out_step_hidden, out_mean_cell
])
self.l_mean = l_mean
self.l_log_std = l_log_std
self.input_dim = input_dim
self.action_dim = action_dim
self.hidden_dim = hidden_dim
self.prev_actions = None
self.prev_hiddens = None
self.prev_cells = None
self.dist = RecurrentDiagonalGaussian(action_dim)
out_layers = [l_mean, l_log_std]
if feature_network is not None:
out_layers.append(feature_network.output_layer)
LayersPowered.__init__(self, out_layers)
def __init__(self,
input_shape,
output_dim,
hidden_dim,
name=None,
hidden_nonlinearity=tf.nn.relu,
lstm_layer_cls=ly.LSTMLayer,
output_nonlinearity=None,
input_var=None,
input_layer=None,
forget_bias=1.0,
use_peepholes=False,
layer_args=None):
with tf.variable_scope(name, "LSTMNetwork"):
if input_layer is None:
l_in = ly.InputLayer(
shape=(None, None) + input_shape,
input_var=input_var,
name="input")
else:
l_in = input_layer
l_step_input = ly.InputLayer(
shape=(None, ) + input_shape, name="step_input")
# contains previous hidden and cell state
l_step_prev_state = ly.InputLayer(
shape=(None, hidden_dim * 2), name="step_prev_state")
if layer_args is None:
layer_args = dict()
l_lstm = lstm_layer_cls(
l_in,
num_units=hidden_dim,
hidden_nonlinearity=hidden_nonlinearity,
hidden_init_trainable=False,
name="lstm_layer",
forget_bias=forget_bias,
cell_init_trainable=False,
use_peepholes=use_peepholes,
**layer_args)
l_lstm_flat = ly.ReshapeLayer(
l_lstm, shape=(-1, hidden_dim), name="lstm_flat")
l_output_flat = ly.DenseLayer(
l_lstm_flat,
num_units=output_dim,
nonlinearity=output_nonlinearity,
name="output_flat")
l_output = ly.OpLayer(
l_output_flat,
op=lambda flat_output, l_input: tf.reshape(
flat_output,
tf.stack((tf.shape(l_input)[0], tf.shape(l_input)[1], -1))
),
shape_op=lambda flat_output_shape, l_input_shape: (
l_input_shape[0], l_input_shape[1], flat_output_shape[-1]),
extras=[l_in],
name="output")
l_step_state = l_lstm.get_step_layer(
l_step_input, l_step_prev_state, name="step_state")
l_step_hidden = ly.SliceLayer(
l_step_state, indices=slice(hidden_dim), name="step_hidden")
l_step_cell = ly.SliceLayer(
l_step_state,
indices=slice(hidden_dim, None),
name="step_cell")
l_step_output = ly.DenseLayer(
l_step_hidden,
num_units=output_dim,
nonlinearity=output_nonlinearity,
w=l_output_flat.w,
b=l_output_flat.b,
name="step_output")
self._l_in = l_in
self._hid_init_param = l_lstm.h0
self._cell_init_param = l_lstm.c0
self._l_lstm = l_lstm
self._l_out = l_output
self._l_step_input = l_step_input
self._l_step_prev_state = l_step_prev_state
self._l_step_hidden = l_step_hidden
self._l_step_cell = l_step_cell
self._l_step_state = l_step_state
self._l_step_output = l_step_output
self._hidden_dim = hidden_dim
