def __init__(
self,
name,
env_spec,
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)
with tf.variable_scope(name):
obs_dim = env_spec.observation_space.flat_dim
action_dim = env_spec.action_space.flat_dim
# create network
if mean_network is None:
mean_network = MLP(
name="mean_network",
input_shape=(obs_dim,),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
)
self._mean_network = mean_network
l_mean = mean_network.output_layer
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="std_network",
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
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
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 = dist_info_sym["mean"]
log_std_var = dist_info_sym["log_std"]
self._f_dist = tensor_utils.compile_function(
inputs=[obs_var],
outputs=[mean_var, log_std_var],
)
def __init__(
self,
name,
env_spec,
hidden_dim=32,
feature_network=None,
state_include_action=True,
hidden_nonlinearity=tf.tanh,
learn_std=True,
init_std=1.0,
output_nonlinearity=None,
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:
"""
with tf.variable_scope(name):
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))
mean_network = LSTMNetwork(input_shape=(feature_dim, ),
input_layer=l_feature,
output_dim=action_dim,
hidden_dim=hidden_dim,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
lstm_layer_cls=lstm_layer_cls,
name="mean_network")
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})
self.f_step_mean_std = tensor_utils.compile_function(
[
flat_input_var,
mean_network.step_prev_state_layer.input_var,
],
L.get_output([
mean_network.step_output_layer, l_step_log_std,
mean_network.step_hidden_layer,
mean_network.step_cell_layer
], {mean_network.step_input_layer: feature_var}))
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 = [mean_network.output_layer, l_log_std]
if feature_network is not None:
out_layers.append(feature_network.output_layer)
LayersPowered.__init__(self, out_layers)
def __init__(self,
env_spec,
name='QuadraticPhinet',
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.relu,
output_nonlinearity=None,
vs_form=None,
bn=False,
A=None,
init_a=1.0,
a_parameterization='exp'):
Serializable.quick_init(self, locals())
assert not env_spec.action_space.is_discrete
self._env_spec = env_spec
self.vs_form = vs_form
with tf.variable_scope(name):
obs_dim = env_spec.observation_space.flat_dim
action_dim = env_spec.action_space.flat_dim
l_act = L.InputLayer(shape=(None, action_dim), name="action")
action_var = l_act.input_var
l_obs = L.InputLayer(shape=(None, obs_dim), name="obs")
self.obs_rms = RunningMeanStd(shape=(obs_dim, ))
obz = L.NormalizeLayer(l_obs, rms=self.obs_rms)
l_hidden = l_obs
hidden_sizes += (action_dim, )
for idx, size in enumerate(hidden_sizes):
if bn:
l_hidden = batch_norm(l_hidden)
l_hidden = L.DenseLayer(l_hidden,
num_units=size,
nonlinearity=hidden_nonlinearity,
name="h%d" % (idx + 1))
obs_var = l_obs.input_var
fs = l_hidden # fs_network.output_layer
if A is not None:
l_A_param = A.output_layer
else:
if a_parameterization == 'exp':
init_a_param = np.log(init_a) - .5
elif a_parameterization == 'softplus':
init_a_param = np.log(np.exp(init_a) - 1)
else:
raise NotImplementedError
l_log_A = L.ParamLayer(
l_obs,
num_units=action_dim,
param=tf.constant_initializer(init_a_param),
name="diagonal_a_matrix",
trainable=True)
if vs_form is not None:
raise NotImplementedError
self._l_log_A = l_log_A
self.a_parameterization = a_parameterization
self.fs = fs
if vs_form is not None:
self._output_vs = vs
LayersPowered.__init__(
self, [self.fs, self._l_log_A, self._output_vs])
else:
LayersPowered.__init__(self, [self.fs, self._l_log_A])
output_var = self.get_phival_sym(obs_var, action_var)
self._f_phival = tensor_utils.compile_function(
inputs=[obs_var, action_var], outputs=output_var)
def __init__(self,
name,
env_spec,
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)
with tf.variable_scope(name):
obs_dim = env_spec.observation_space.flat_dim
action_dim = env_spec.action_space.flat_dim
# create network
if mean_network is None:
# returns layers(), input_layer, output_layer, input_var, output
mean_network = self.create_MLP(
name="mean_network",
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
)
else:
raise NotImplementedError('Chelsea does not support this.')
l_mean = mean_network[2]
obs_var = mean_network[3]
if std_network is not None:
raise NotImplementedError(
'Contained Gaussian MLP does not support this.')
l_std_param = std_network.output_layer
else:
if adaptive_std:
# returns layers(), input_layer, output_layer, input_var, output
std_network = self.create_MLP(
name="std_network",
input_shape=(obs_dim, ),
input_layer=mean_network[1],
output_dim=action_dim,
hidden_sizes=std_hidden_sizes,
hidden_nonlinearity=std_hidden_nonlinearity,
output_nonlinearity=None,
)
l_std_param = std_network[2]
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
l_std_param = L.ParamLayer(
mean_network[1],
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
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])
self._output_layers = [l_mean, l_std_param]
#self._input_layers = None
# Parameterized.__init__(self)
self._cached_params = {}
#self._cached_param_dtypes = {} #self._cached_param_shapes = {} #self._cached_assign_ops = {} #self._cached_assign_placeholders = {}
super(GaussianMLPPolicy, self).__init__(env_spec)
dist_info_sym = self.dist_info_sym(mean_network[3], dict())
mean_var = dist_info_sym["mean"]
log_std_var = dist_info_sym["log_std"]
def f_dist(*input_vals):
sess = tf.get_default_session()
return sess.run([mean_var, log_std_var],
feed_dict=dict(list(zip([obs_var],
input_vals))))
self._f_dist = f_dist
def __init__(
self,
name,
input_shape,
output_dim,
# observation_space,
mean_network=None,
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.tanh,
optimizer=None,
use_trust_region=True,
step_size=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 step_size: 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 learned.
: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.
"""
Serializable.quick_init(self, locals())
with tf.variable_scope(name):
if optimizer is None:
if use_trust_region:
optimizer = PenaltyLbfgsOptimizer("optimizer")
else:
optimizer = LbfgsOptimizer("optimizer")
self._optimizer = optimizer
self._subsample_factor = subsample_factor
if mean_network is None:
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
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",
)
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
# self.observation_space = observation_space
normalized_xs_var = (xs_var - x_mean_var) / x_std_var
normalized_ys_var = (ys_var - y_mean_var) / y_std_var
normalized_means_var = L.get_output(l_mean, {mean_network.input_layer: 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, step_size)
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
self.input_dim = input_shape[0]
self.output_dim = output_dim
