def update_opt(self, f, target, inputs, reg_coeff):
self.target = target
self.reg_coeff = reg_coeff
params = target.get_params(trainable=True)
constraint_grads = theano.grad(
f, wrt=params, disconnected_inputs='warn')
xs = tuple([ext.new_tensor_like("%s x" % p.name, p) for p in params])
def Hx_plain():
Hx_plain_splits = TT.grad(
TT.sum([TT.sum(g * x)
for g, x in zip(constraint_grads, xs)]),
wrt=params,
disconnected_inputs='warn'
)
return TT.concatenate([TT.flatten(s) for s in Hx_plain_splits])
self.opt_fun = ext.lazydict(
f_Hx_plain=lambda: ext.compile_function(
inputs=inputs + xs,
outputs=Hx_plain(),
log_name="f_Hx_plain",
),
)
def update_opt(self, loss, target, inputs, extra_inputs=None, **kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:return: No return value.
"""
self._target = target
updates = self._update_method(loss, target.get_params(trainable=True))
updates = OrderedDict([(k, v.astype(k.dtype)) for k, v in list(updates.items())])
if extra_inputs is None:
extra_inputs = list()
self._opt_fun = ext.lazydict(
f_loss=lambda: ext.compile_function(inputs + extra_inputs, loss),
f_opt=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=loss,
updates=updates,
)
)
def update_opt(self, loss, target, inputs, extra_inputs=None, *args, **kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:return: No return value.
"""
self._target = target
def get_opt_output():
flat_grad = tensor_utils.flatten_tensor_variables(tf.gradients(loss, target.get_params(trainable=True)))
return [tf.cast(loss, tf.float64), tf.cast(flat_grad, tf.float64)]
if extra_inputs is None:
extra_inputs = list()
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(inputs + extra_inputs, loss),
f_opt=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=get_opt_output(),
)
)
def update_opt(self, loss, target, inputs, extra_inputs=None, gradients=None, *args, **kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:param gradients: symbolic expressions for the gradients of trainable parameters of the target. By default
this will be computed by calling theano.grad
:return: No return value.
"""
self._target = target
def get_opt_output(gradients):
if gradients is None:
gradients = theano.grad(loss, target.get_params(trainable=True))
flat_grad = flatten_tensor_variables(gradients)
return [loss.astype('float64'), flat_grad.astype('float64')]
if extra_inputs is None:
extra_inputs = list()
self._opt_fun = lazydict(
f_loss=lambda: compile_function(inputs + extra_inputs, loss),
f_opt=lambda: compile_function(
inputs=inputs + extra_inputs,
outputs=get_opt_output(gradients),
)
)
def update_opt(self, f, target, inputs, reg_coeff):
self.target = target
self.reg_coeff = reg_coeff
params = target.get_params(trainable=True)
constraint_grads = theano.grad(
f, wrt=params, disconnected_inputs='warn')
xs = tuple([ext.new_tensor_like("%s x" % p.name, p) for p in params])
def Hx_plain():
Hx_plain_splits = TT.grad(
TT.sum([TT.sum(g * x)
for g, x in zip(constraint_grads, xs)]),
wrt=params,
disconnected_inputs='warn'
)
return TT.concatenate([TT.flatten(s) for s in Hx_plain_splits])
self.opt_fun = ext.lazydict(
f_Hx_plain=lambda: ext.compile_function(
inputs=inputs + xs,
outputs=Hx_plain(),
log_name="f_Hx_plain",
),
)
def update_opt(self, f, target, inputs, reg_coeff):
self.target = target
self.reg_coeff = reg_coeff
params = target.get_params(trainable=True)
constraint_grads = tf.gradients(f, xs=params)
for idx, (grad, param) in enumerate(zip(constraint_grads, params)):
if grad is None:
constraint_grads[idx] = tf.zeros_like(param)
xs = tuple([tensor_utils.new_tensor_like(p.name.split(":")[0], p) for p in params])
def Hx_plain():
Hx_plain_splits = tf.gradients(
tf.reduce_sum(
tf.stack([tf.reduce_sum(g * x) for g, x in zip(constraint_grads, xs)])
),
params
)
for idx, (Hx, param) in enumerate(zip(Hx_plain_splits, params)):
if Hx is None:
Hx_plain_splits[idx] = tf.zeros_like(param)
return tensor_utils.flatten_tensor_variables(Hx_plain_splits)
self.opt_fun = ext.lazydict(
f_Hx_plain=lambda: tensor_utils.compile_function(
inputs=inputs + xs,
outputs=Hx_plain(),
log_name="f_Hx_plain",
),
)
def update_opt(self,
loss,
target,
inputs,
extra_inputs=None,
*args,
**kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:return: No return value.
"""
if config.TF_NN_SETTRACE:
ipdb.set_trace()
self._target = target
def get_opt_output():
flat_grad = tensor_utils.flatten_tensor_variables(
tf.gradients(loss, target.get_params(trainable=True)))
return [tf.cast(loss, tf.float64), tf.cast(flat_grad, tf.float64)]
if extra_inputs is None:
extra_inputs = list()
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(
inputs + extra_inputs, loss),
f_opt=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=get_opt_output(),
))
def update_opt(self, loss, target, inputs, extra_inputs=None, **kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:return: No return value.
"""
self._target = target
updates = self._update_method(loss, target.get_params(trainable=True))
updates = OrderedDict([(k, v.astype(k.dtype))
for k, v in updates.iteritems()])
if extra_inputs is None:
extra_inputs = list()
self._opt_fun = ext.lazydict(
f_loss=lambda: ext.compile_function(inputs + extra_inputs, loss),
f_opt=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=loss,
updates=updates,
))
def update_opt(self, f, target, inputs, reg_coeff):
self.target = target
self.reg_coeff = reg_coeff
params = target.get_params(trainable=True)
constraint_grads = tf.gradients(f, xs=params)
for idx, (grad, param) in enumerate(zip(constraint_grads, params)):
if grad is None:
constraint_grads[idx] = tf.zeros_like(param)
xs = tuple([
tensor_utils.new_tensor_like(p.name.split(":")[0], p)
for p in params
])
def Hx_plain():
Hx_plain_splits = tf.gradients(
tf.reduce_sum(
tf.stack([
tf.reduce_sum(g * x)
for g, x in zip(constraint_grads, xs)
])), params)
for idx, (Hx, param) in enumerate(zip(Hx_plain_splits, params)):
if Hx is None:
Hx_plain_splits[idx] = tf.zeros_like(param)
return tensor_utils.flatten_tensor_variables(Hx_plain_splits)
self.opt_fun = ext.lazydict(
f_Hx_plain=lambda: tensor_utils.compile_function(
inputs=inputs + xs,
outputs=Hx_plain(),
log_name="f_Hx_plain",
), )
def update_opt(
self, loss, target, leq_constraint, inputs, extra_inputs=None, constraint_name="constraint", *args, **kwargs
):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs, which could be subsampled if needed. It is assumed
that the first dimension of these inputs should correspond to the number of data points
:param extra_inputs: A list of symbolic variables as extra inputs which should not be subsampled
:return: No return value.
"""
inputs = tuple(inputs)
if extra_inputs is None:
extra_inputs = tuple()
else:
extra_inputs = tuple(extra_inputs)
constraint_term, constraint_value = leq_constraint
params = target.get_params(trainable=True)
grads = theano.grad(loss, wrt=params)
flat_grad = ext.flatten_tensor_variables(grads)
constraint_grads = theano.grad(constraint_term, wrt=params)
xs = tuple([ext.new_tensor_like("%s x" % p.name, p) for p in params])
Hx_plain_splits = TT.grad(TT.sum([TT.sum(g * x) for g, x in itertools.izip(constraint_grads, xs)]), wrt=params)
Hx_plain = TT.concatenate([TT.flatten(s) for s in Hx_plain_splits])
self._target = target
self._max_constraint_val = constraint_value
self._constraint_name = constraint_name
if self._debug_nan:
from theano.compile.nanguardmode import NanGuardMode
mode = NanGuardMode(nan_is_error=True, inf_is_error=True, big_is_error=True)
else:
mode = None
self._opt_fun = ext.lazydict(
f_loss=lambda: ext.compile_function(
inputs=inputs + extra_inputs, outputs=loss, log_name="f_loss", mode=mode
),
f_grad=lambda: ext.compile_function(
inputs=inputs + extra_inputs, outputs=flat_grad, log_name="f_grad", mode=mode
),
f_Hx_plain=lambda: ext.compile_function(
inputs=inputs + extra_inputs + xs, outputs=Hx_plain, log_name="f_Hx_plain", mode=mode
),
f_constraint=lambda: ext.compile_function(
inputs=inputs + extra_inputs, outputs=constraint_term, log_name="constraint", mode=mode
),
f_loss_constraint=lambda: ext.compile_function(
inputs=inputs + extra_inputs, outputs=[loss, constraint_term], log_name="f_loss_constraint", mode=mode
),
)
def update_opt(self,
loss,
target,
logstd,
inputs,
extra_inputs=None,
**kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:return: No return value.
"""
self._target = target
self._log_std = tf.reduce_mean(logstd)
if extra_inputs is None:
extra_inputs = list()
self._input_vars = inputs + extra_inputs
# \partial{log \pi} / \partial{\phi} A
# \phi is the mean_network parameters
# pdb.set_trace()
mean_w = target.get_mean_network().get_params(trainable=True)
grads = tf.gradients(
loss, xs=target.get_mean_network().get_params(trainable=True))
for idx, (g, param) in enumerate(zip(grads, mean_w)):
if g is None:
grads[idx] = tf.zeros_like(param)
flat_grad = tensor_utils.flatten_tensor_variables(grads)
# \sum_d \partial{logstd^d} / \partial{\phi}
# \phi is the std_network parameters
var_grads = tf.gradients(
loss - self._alpha * self._log_std,
xs=target.get_std_network().get_params(trainable=True))
var_w = target.get_std_network().get_params(trainable=True)
for idx, (g, param) in enumerate(zip(var_grads, var_w)):
if g is None:
var_grads[idx] = tf.zeros_like(param)
flat_var_grad = tensor_utils.flatten_tensor_variables(var_grads)
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(
inputs + extra_inputs, loss),
f_grad=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=flat_grad,
),
f_var_grad=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=flat_var_grad,
),
)
def update_opt(self, loss, target, leq_constraint, inputs, extra_inputs=None, constraint_name="constraint", *args,
**kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs, which could be subsampled if needed. It is assumed
that the first dimension of these inputs should correspond to the number of data points
:param extra_inputs: A list of symbolic variables as extra inputs which should not be subsampled
:return: No return value.
"""
inputs = tuple(inputs)
if extra_inputs is None:
extra_inputs = tuple()
else:
extra_inputs = tuple(extra_inputs)
constraint_term, constraint_value = leq_constraint
params = target.get_params(trainable=True)
grads = tf.gradients(loss, xs=params)
for idx, (grad, param) in enumerate(zip(grads, params)):
if grad is None:
grads[idx] = tf.zeros_like(param)
flat_grad = tensor_utils.flatten_tensor_variables(grads)
self._hvp_approach.update_opt(f=constraint_term, target=target, inputs=inputs + extra_inputs,
reg_coeff=self._reg_coeff)
self._target = target
self._max_constraint_val = constraint_value
self._constraint_name = constraint_name
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=loss,
log_name="f_loss",
),
f_grad=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=flat_grad,
log_name="f_grad",
),
f_constraint=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=constraint_term,
log_name="constraint",
),
f_loss_constraint=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=[loss, constraint_term],
log_name="f_loss_constraint",
),
)
def update_opt(self, loss, target, leq_constraint, inputs, extra_inputs=None, constraint_name="constraint", *args,
**kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs, which could be subsampled if needed. It is assumed
that the first dimension of these inputs should correspond to the number of data points
:param extra_inputs: A list of symbolic variables as extra inputs which should not be subsampled
:return: No return value.
"""
inputs = tuple(inputs)
if extra_inputs is None:
extra_inputs = tuple()
else:
extra_inputs = tuple(extra_inputs)
constraint_term, constraint_value = leq_constraint
params = target.get_params(trainable=True)
grads = tf.gradients(loss, xs=params)
for idx, (grad, param) in enumerate(zip(grads, params)):
if grad is None:
grads[idx] = tf.zeros_like(param)
flat_grad = tensor_utils.flatten_tensor_variables(grads)
self._hvp_approach.update_opt(f=constraint_term, target=target, inputs=inputs + extra_inputs,
reg_coeff=self._reg_coeff)
self._target = target
self._max_constraint_val = constraint_value
self._constraint_name = constraint_name
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=loss,
log_name="f_loss",
),
f_grad=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=flat_grad,
log_name="f_grad",
),
f_constraint=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=constraint_term,
log_name="constraint",
),
f_loss_constraint=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=[loss, constraint_term],
log_name="f_loss_constraint",
),
)
def update_opt(self,
loss,
target,
leq_constraint,
inputs,
extra_inputs=None,
**kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:policy network with parameter w to optimize
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:return: No return value.
"""
if extra_inputs is None:
extra_inputs = list()
self._input_vars = inputs + extra_inputs
self._target = target
constraint_term, constraint_value = leq_constraint
self._max_constraint_val = constraint_value
w = target.get_params(trainable=True)
grads = tf.gradients(loss, xs=w)
for idx, (g, param) in enumerate(zip(grads, w)):
if g is None:
grads[idx] = tf.zeros_like(param)
flat_grad = tensor_utils.flatten_tensor_variables(grads)
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=loss,
),
f_grad=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=flat_grad,
),
f_loss_constraint=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=[loss, constraint_term],
),
)
inputs = tuple(inputs)
if extra_inputs is None:
extra_inputs = tuple()
else:
extra_inputs = tuple(extra_inputs)
self._hvp_approach.update_opt(f=constraint_term,
target=target,
inputs=inputs + extra_inputs,
reg_coeff=self._reg_coeff)
def update_opt(self,
loss,
target,
leq_constraint,
inputs,
constraint_name="constraint",
*args,
**kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:return: No return value.
"""
if config.TF_NN_SETTRACE:
ipdb.set_trace()
constraint_term, constraint_value = leq_constraint
with tf.variable_scope(self._name):
penalty_var = tf.placeholder(tf.float32, tuple(), name="penalty")
penalized_loss = loss + penalty_var * constraint_term
self._target = target
self._max_constraint_val = constraint_value
self._constraint_name = constraint_name
def get_opt_output():
params = target.get_params(trainable=True)
grads = tf.gradients(penalized_loss, params)
for idx, (grad, param) in enumerate(zip(grads, params)):
if grad is None:
grads[idx] = tf.zeros_like(param)
flat_grad = tensor_utils.flatten_tensor_variables(grads)
return [
tf.cast(penalized_loss, tf.float64),
tf.cast(flat_grad, tf.float64),
]
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(
inputs, loss, log_name="f_loss"),
f_constraint=lambda: tensor_utils.compile_function(
inputs, constraint_term, log_name="f_constraint"),
f_penalized_loss=lambda: tensor_utils.compile_function(
inputs=inputs + [penalty_var],
outputs=[penalized_loss, loss, constraint_term],
log_name="f_penalized_loss",
),
f_opt=lambda: tensor_utils.compile_function(
inputs=inputs + [penalty_var],
outputs=get_opt_output(),
))
def update_opt(self,
loss,
target,
leq_constraint,
inputs,
constraint_name="constraint",
*args,
**kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:return: No return value.
"""
constraint_terms = [c[0] for c in leq_constraint]
constraint_values = [c[1] for c in leq_constraint]
penalty_var = TT.scalar("penalty")
penalty_loss = constraint_terms[0]
for i in range(1, len(constraint_terms)):
penalty_loss += constraint_terms[i]
penalized_loss = loss + penalty_var * penalty_loss
self._target = target
self._max_constraint_vals = np.array(constraint_values)
self._constraint_name = constraint_name
def get_opt_output():
flat_grad = flatten_tensor_variables(
theano.grad(penalized_loss,
target.get_params(trainable=True),
disconnected_inputs='ignore'))
return [
penalized_loss.astype('float64'),
flat_grad.astype('float64')
]
self._opt_fun = lazydict(
f_loss=lambda: compile_function(inputs, loss, log_name="f_loss"),
f_constraint=lambda: compile_function(
inputs, penalty_loss, log_name="f_constraint"),
f_penalized_loss=lambda: compile_function(
inputs=inputs + [penalty_var],
outputs=[penalized_loss, loss] + constraint_terms,
log_name="f_penalized_loss",
),
f_opt=lambda: compile_function(inputs=inputs + [penalty_var],
outputs=get_opt_output(),
log_name="f_opt"))
def update_opt(self, loss, target, inputs, extra_inputs=None, **kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:return: No return value.
"""
if config.TF_NN_SETTRACE:
ipdb.set_trace()
self._target = target
self._train_op = self._tf_optimizer.minimize(
loss, var_list=target.get_params(trainable=True))
# define operations for updating prior.
update_mus = [(l.bayesreg.hyperparams['empirical'],
l.bayesreg.w_mu.assign(l.W_mu)) for l in target.layers
if hasattr(l, 'bayesreg')]
update_rhos = [(l.bayesreg.hyperparams['empirical'],
l.bayesreg.w_sig.assign(tf.log(1.0 + tf.exp(l.W_rho))))
for l in target.layers if hasattr(l, 'bayesreg')]
self._update_priors_ops = update_mus + update_rhos
# updates = OrderedDict([(k, v.astype(k.dtype)) for k, v in updates.iteritems()])
if extra_inputs is None:
extra_inputs = list()
self._input_vars = inputs + extra_inputs
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(
inputs + extra_inputs, loss), )
if kwargs.has_key('like_loss'):
def l_loss():
return tensor_utils.compile_function(inputs + extra_inputs,
kwargs['like_loss'])
self._opt_fun.set('l_loss', l_loss)
if kwargs.has_key('cmpx_loss'):
def c_loss():
return tensor_utils.compile_function(inputs + extra_inputs,
kwargs['cmpx_loss'])
self._opt_fun.set('c_loss', c_loss)
def update_opt(self,
loss,
target,
inputs,
extra_inputs=None,
vars_to_optimize=None,
**kwargs):
# Initializes the update opt used in the optimization
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:return: No return value.
"""
self._target = target
if vars_to_optimize is None:
vars_to_optimize = target.get_params(trainable=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if update_ops:
# for batch norm
updates = tf.group(*update_ops)
with tf.control_dependencies([updates]):
self._train_op = self._tf_optimizer.minimize(
loss, var_list=vars_to_optimize)
if self._init_tf_optimizer is not None:
self._init_train_op = self._init_tf_optimizer.minimize(
loss, var_list=vars_to_optimize)
else:
self._train_op = self._tf_optimizer.minimize(
loss, var_list=vars_to_optimize)
if self._init_tf_optimizer is not None:
self._init_train_op = self._init_tf_optimizer.minimize(
loss, var_list=vars_to_optimize)
if extra_inputs is None:
extra_inputs = list()
self._input_vars = inputs + extra_inputs
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(
inputs + extra_inputs, loss), )
self.debug_loss = loss
self.debug_vars = target.get_params(trainable=True)
self.debug_target = target
def update_opt(self, f, target, inputs, reg_coeff):
if config.TF_NN_SETTRACE:
ipdb.set_trace()
self.target = target
self.reg_coeff = reg_coeff
params = target.get_params(trainable=True)
constraint_grads = tf.gradients(f, xs=params)
for idx, (grad, param) in enumerate(zip(constraint_grads, params)):
if grad is None:
constraint_grads[idx] = tf.zeros_like(param)
flat_grad = tensor_utils.flatten_tensor_variables(constraint_grads)
def f_Hx_plain(*args):
inputs_ = args[:len(inputs)]
xs = args[len(inputs):]
flat_xs = np.concatenate([np.reshape(x, (-1, )) for x in xs])
param_val = self.target.get_param_values(trainable=True)
eps = np.cast['float32'](self.base_eps /
(np.linalg.norm(param_val) + 1e-8))
self.target.set_param_values(param_val + eps * flat_xs,
trainable=True)
flat_grad_dvplus = self.opt_fun["f_grad"](*inputs_)
self.target.set_param_values(param_val, trainable=True)
if self.symmetric:
self.target.set_param_values(param_val - eps * flat_xs,
trainable=True)
flat_grad_dvminus = self.opt_fun["f_grad"](*inputs_)
hx = (flat_grad_dvplus - flat_grad_dvminus) / (2 * eps)
self.target.set_param_values(param_val, trainable=True)
else:
flat_grad = self.opt_fun["f_grad"](*inputs_)
hx = (flat_grad_dvplus - flat_grad) / eps
return hx
self.opt_fun = ext.lazydict(
f_grad=lambda: tensor_utils.compile_function(
inputs=inputs,
outputs=flat_grad,
log_name="f_grad",
),
f_Hx_plain=lambda: f_Hx_plain,
)
def update_opt(self, loss, target, leq_constraint, inputs, extra_inputs=None, constraint_name="constraint", *args,
**kwargs):
inputs = tuple(inputs)
if extra_inputs is None:
extra_inputs = tuple()
else:
extra_inputs = tuple(extra_inputs)
constraint_term, constraint_value = leq_constraint
params = target.get_params(trainable=True)
grads = theano.grad(loss, wrt=params, disconnected_inputs='warn')
flat_grad = ext.flatten_tensor_variables(grads)
self._hvp_approach.update_opt(f=constraint_term, target=target, inputs=inputs + extra_inputs,
reg_coeff=self._reg_coeff)
self._target = target
self._max_constraint_val = constraint_value
self._constraint_name = constraint_name
self._opt_fun = ext.lazydict(
f_loss=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=loss,
log_name="f_loss",
),
f_grad=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=flat_grad,
log_name="f_grad",
),
f_constraint=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=constraint_term,
log_name="constraint",
),
f_loss_constraint=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=[loss, constraint_term],
log_name="f_loss_constraint",
),
)
def update_opt(self, loss, target, leq_constraint, inputs, constraint_name="constraint", *args, **kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:return: No return value.
"""
constraint_term, constraint_value = leq_constraint
with tf.variable_scope(self._name):
penalty_var = tf.placeholder(tf.float32, tuple(), name="penalty")
penalized_loss = loss + penalty_var * constraint_term
self._target = target
self._max_constraint_val = constraint_value
self._constraint_name = constraint_name
def get_opt_output():
params = target.get_params(trainable=True)
grads = tf.gradients(penalized_loss, params)
for idx, (grad, param) in enumerate(zip(grads, params)):
if grad is None:
grads[idx] = tf.zeros_like(param)
flat_grad = tensor_utils.flatten_tensor_variables(grads)
return [
tf.cast(penalized_loss, tf.float64),
tf.cast(flat_grad, tf.float64),
]
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(inputs, loss, log_name="f_loss"),
f_constraint=lambda: tensor_utils.compile_function(inputs, constraint_term, log_name="f_constraint"),
f_penalized_loss=lambda: tensor_utils.compile_function(
inputs=inputs + [penalty_var],
outputs=[penalized_loss, loss, constraint_term],
log_name="f_penalized_loss",
),
f_opt=lambda: tensor_utils.compile_function(
inputs=inputs + [penalty_var],
outputs=get_opt_output(),
)
)
def update_opt(self, f, target, inputs, reg_coeff):
self.target = target
self.reg_coeff = reg_coeff
params = target.get_params(trainable=True)
constraint_grads = theano.grad(f,
wrt=params,
disconnected_inputs='warn')
flat_grad = ext.flatten_tensor_variables(constraint_grads)
def f_Hx_plain(*args):
inputs_ = args[:len(inputs)]
xs = args[len(inputs):]
flat_xs = np.concatenate([np.reshape(x, (-1, )) for x in xs])
param_val = self.target.get_param_values(trainable=True)
eps = np.cast['float32'](self.base_eps /
(np.linalg.norm(param_val) + 1e-8))
self.target.set_param_values(param_val + eps * flat_xs,
trainable=True)
flat_grad_dvplus = self.opt_fun["f_grad"](*inputs_)
if self.symmetric:
self.target.set_param_values(param_val - eps * flat_xs,
trainable=True)
flat_grad_dvminus = self.opt_fun["f_grad"](*inputs_)
hx = (flat_grad_dvplus - flat_grad_dvminus) / (2 * eps)
self.target.set_param_values(param_val, trainable=True)
else:
self.target.set_param_values(param_val, trainable=True)
flat_grad = self.opt_fun["f_grad"](*inputs_)
hx = (flat_grad_dvplus - flat_grad) / eps
return hx
self.opt_fun = ext.lazydict(
f_grad=lambda: ext.compile_function(
inputs=inputs,
outputs=flat_grad,
log_name="f_grad",
),
f_Hx_plain=lambda: f_Hx_plain,
)
def update_opt(self, f, target, inputs, reg_coeff):
self.target = target
self.reg_coeff = reg_coeff
params = target.get_params(trainable=True)
constraint_grads = theano.grad(
f, wrt=params, disconnected_inputs='warn')
flat_grad = ext.flatten_tensor_variables(constraint_grads)
def f_Hx_plain(*args):
inputs_ = args[:len(inputs)]
xs = args[len(inputs):]
flat_xs = np.concatenate([np.reshape(x, (-1,)) for x in xs])
param_val = self.target.get_param_values(trainable=True)
eps = np.cast['float32'](
self.base_eps / (np.linalg.norm(param_val) + 1e-8))
self.target.set_param_values(
param_val + eps * flat_xs, trainable=True)
flat_grad_dvplus = self.opt_fun["f_grad"](*inputs_)
if self.symmetric:
self.target.set_param_values(
param_val - eps * flat_xs, trainable=True)
flat_grad_dvminus = self.opt_fun["f_grad"](*inputs_)
hx = (flat_grad_dvplus - flat_grad_dvminus) / (2 * eps)
self.target.set_param_values(param_val, trainable=True)
else:
self.target.set_param_values(param_val, trainable=True)
flat_grad = self.opt_fun["f_grad"](*inputs_)
hx = (flat_grad_dvplus - flat_grad) / eps
return hx
self.opt_fun = ext.lazydict(
f_grad=lambda: ext.compile_function(
inputs=inputs,
outputs=flat_grad,
log_name="f_grad",
),
f_Hx_plain=lambda: f_Hx_plain,
)
def update_opt(self, f, target, inputs, reg_coeff):
self.target = target
self.reg_coeff = reg_coeff
params = target.get_params(trainable=True)
constraint_grads = tf.gradients(f, xs=params)
for idx, (grad, param) in enumerate(zip(constraint_grads, params)):
if grad is None:
constraint_grads[idx] = tf.zeros_like(param)
flat_grad = tensor_utils.flatten_tensor_variables(constraint_grads)
def f_Hx_plain(*args):
inputs_ = args[:len(inputs)]
xs = args[len(inputs):]
flat_xs = np.concatenate([np.reshape(x, (-1,)) for x in xs])
param_val = self.target.get_param_values(trainable=True)
eps = np.cast['float32'](self.base_eps / (np.linalg.norm(param_val) + 1e-8))
self.target.set_param_values(param_val + eps * flat_xs, trainable=True)
flat_grad_dvplus = self.opt_fun["f_grad"](*inputs_)
self.target.set_param_values(param_val, trainable=True)
if self.symmetric:
self.target.set_param_values(param_val - eps * flat_xs, trainable=True)
flat_grad_dvminus = self.opt_fun["f_grad"](*inputs_)
hx = (flat_grad_dvplus - flat_grad_dvminus) / (2 * eps)
self.target.set_param_values(param_val, trainable=True)
else:
flat_grad = self.opt_fun["f_grad"](*inputs_)
hx = (flat_grad_dvplus - flat_grad) / eps
return hx
self.opt_fun = ext.lazydict(
f_grad=lambda: tensor_utils.compile_function(
inputs=inputs,
outputs=flat_grad,
log_name="f_grad",
),
f_Hx_plain=lambda: f_Hx_plain,
)
def update_opt(self, loss, target, leq_constraint, inputs, constraint_name="constraint", *args, **kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:return: No return value.
"""
constraint_term, constraint_value = leq_constraint
penalty_var = TT.scalar("penalty")
penalized_loss = loss + penalty_var * constraint_term
self._target = target
self._max_constraint_val = constraint_value
self._constraint_name = constraint_name
def get_opt_output():
flat_grad = flatten_tensor_variables(theano.grad(
penalized_loss, target.get_params(trainable=True), disconnected_inputs='ignore'
))
return [penalized_loss.astype('float64'), flat_grad.astype('float64')]
self._opt_fun = lazydict(
f_loss=lambda: compile_function(inputs, loss, log_name="f_loss"),
f_constraint=lambda: compile_function(inputs, constraint_term, log_name="f_constraint"),
f_penalized_loss=lambda: compile_function(
inputs=inputs + [penalty_var],
outputs=[penalized_loss, loss, constraint_term],
log_name="f_penalized_loss",
),
f_opt=lambda: compile_function(
inputs=inputs + [penalty_var],
outputs=get_opt_output(),
log_name="f_opt"
)
)
def update_opt(self, loss, target, inputs, extra_inputs=None, **kwargs):
# Initializes the update opt used in the optimization
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:return: No return value.
"""
self._target = target
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if update_ops:
# for batch norm
updates = tf.group(*update_ops)
with tf.control_dependencies([updates]):
self._train_op = self._tf_optimizer.minimize(loss, var_list=target.get_params(trainable=True))
if self._init_tf_optimizer is not None:
self._init_train_op = self._init_tf_optimizer.minimize(loss, var_list=target.get_params(trainable=True))
else:
self._train_op = self._tf_optimizer.minimize(loss, var_list=target.get_params(trainable=True))
if self._init_tf_optimizer is not None:
self._init_train_op = self._init_tf_optimizer.minimize(loss, var_list=target.get_params(trainable=True))
if extra_inputs is None:
extra_inputs = list()
self._input_vars = inputs + extra_inputs
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(inputs + extra_inputs, loss),
)
self.debug_loss = loss
self.debug_vars = target.get_params(trainable=True)
self.debug_target = target
def update_opt(self,
loss,
target,
inputs,
extra_inputs=None,
gradients=None,
*args,
**kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:param gradients: symbolic expressions for the gradients of trainable parameters of the target. By default
this will be computed by calling theano.grad
:return: No return value.
"""
self._target = target
def get_opt_output(gradients):
if gradients is None:
gradients = theano.grad(loss,
target.get_params(trainable=True))
flat_grad = flatten_tensor_variables(gradients)
return [loss.astype('float64'), flat_grad.astype('float64')]
if extra_inputs is None:
extra_inputs = list()
self._opt_fun = lazydict(
f_loss=lambda: compile_function(inputs + extra_inputs, loss),
f_opt=lambda: compile_function(
inputs=inputs + extra_inputs,
outputs=get_opt_output(gradients),
))
def update_opt(self, loss, target, inputs, extra_inputs=None):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param inputs: A list of symbolic variables as inputs
:param extra_inputs: A list of symbolic variables as inputs
:return: No return value.
"""
self._target = target
grads = utils.clip_grads(
self._tf_optimizer.compute_gradients(
loss=loss, var_list=target.get_params(trainable=True)),
self.clip_grads)
self._train_op = self._tf_optimizer.apply_gradients(grads)
if extra_inputs is None:
extra_inputs = list()
self._input_vars = inputs + extra_inputs
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(
inputs + extra_inputs, loss), )
def update_opt(self,
loss,
target,
inputs,
extra_inputs=None,
gradients=None,
**kwargs):
self._target = target
if gradients is None:
gradients = theano.grad(loss,
target.get_params(trainable=True),
disconnected_inputs='ignore')
flat_grad = ext.flatten_tensor_variables(gradients)
if extra_inputs is None:
extra_inputs = list()
self._opt_fun = ext.lazydict(f_loss=lambda: ext.compile_function(
inputs + extra_inputs, loss, log_name=self._name + "_f_loss"),
f_grad=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=flat_grad,
log_name=self._name + "_f_grad"))
def update_opt(self, loss, target, inputs, network_outputs, extra_inputs=None):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param inputs: A list of symbolic variables as inputs
:return: No return value.
"""
self._target = target
if extra_inputs is None:
extra_inputs = list()
self._hf_optimizer = hf_optimizer(
_p=target.get_params(trainable=True),
inputs=(inputs + extra_inputs),
s=network_outputs,
costs=[loss],
)
self._opt_fun = lazydict(
f_loss=lambda: compile_function(inputs + extra_inputs, loss),
)
def update_opt(self,
loss,
target,
quad_leq_constraint,
lin_leq_constraint,
inputs,
extra_inputs=None,
constraint_name_1="quad_constraint",
constraint_name_2="lin_constraint",
using_surrogate=False,
true_linear_leq_constraint=None,
precompute=False,
attempt_feasible_recovery=False,
attempt_infeasible_recovery=False,
revert_to_last_safe_point=False,
*args,
**kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param lin_leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
This constraint will be linearized.
:param quad_leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
This constraint will be quadratified.
:param inputs: A list of symbolic variables as inputs, which could be subsampled if needed. It is assumed
that the first dimension of these inputs should correspond to the number of data points
:param extra_inputs: A list of symbolic variables as extra inputs which should not be subsampled
:return: No return value.
All right, on the business of this "using_surrogate" and "true_linear_leq_constraint" stuff...
In rllab, when we optimize a policy, we minimize a "surrogate loss" function (or, if you prefer,
maximize a surrogate return). The surrogate loss function we optimize is
mean( lr * advantage ),
where 'lr' is the likelihood ratio of the new policy with respect to the old policy,
lr(s,a) = pi_new(a|s) / pi_old(a|s).
We choose this surrogate loss function because its gradient is equal to the gradient of the true
objective function when pi_new = pi_old.
However, the real thing we want to optimize is
J(pi) = E_{tau ~ pi} [R(tau)].
If we wanted to measure J(pi_old), it would not suffice to calculate the surrogate loss function at pi_old.
Usually this is not an issue because we don't actually need to compute J(pi_old) at all, because we have no need
for it. But in our optimization procedure here, we need to calculate a directly analogous property -
- the expected safety return - because its value matters for constraint enforcement in our linear approximation.
So, "using_surrogate" and "true_linear_leq_constraint" are here to handle the cases where the "lin_leq_constraint"
argument submitted by the user is really a SURROGATE leq_constraint, which we can get a good gradient from,
but when we need a different symbolic expression to actually evaluate the linear_leq_constraint.
"use_surrogate" is the flag indicating that the lin_leq_constraint argument is in fact a surrogate,
and then "true_linear_leq_constraint" is for the actual value.
:param precompute: Use an 'input' for the linearization constant instead of true_linear_leq_constraint.
If present, overrides surrogate
When using precompute, the last input is the precomputed linearization constant
:param attempt_(in)feasible_recovery: deals with cases where x=0 is infeasible point but problem still feasible
(where optimization problem is entirely infeasible)
:param revert_to_last_safe_point: Behavior protocol for situation when optimization problem is entirely infeasible.
Specifies that we should just reset the parameters to the last point
that satisfied constraint.
"""
self.precompute = precompute
self.attempt_feasible_recovery = attempt_feasible_recovery
self.attempt_infeasible_recovery = attempt_infeasible_recovery
self.revert_to_last_safe_point = revert_to_last_safe_point
inputs = tuple(inputs)
if extra_inputs is None:
extra_inputs = tuple()
else:
extra_inputs = tuple(extra_inputs)
constraint_term_1, constraint_value_1 = quad_leq_constraint
constraint_term_2, constraint_value_2 = lin_leq_constraint
params = target.get_params(trainable=True)
grads = theano.grad(loss, wrt=params, disconnected_inputs='warn')
flat_grad = ext.flatten_tensor_variables(grads)
lin_constraint_grads = theano.grad(constraint_term_2,
wrt=params,
disconnected_inputs='warn')
flat_lin_constraint_grad = ext.flatten_tensor_variables(
lin_constraint_grads)
if using_surrogate and not (precompute):
constraint_term_2 = true_linear_leq_constraint
self._hvp_approach.update_opt(f=constraint_term_1,
target=target,
inputs=inputs + extra_inputs,
reg_coeff=self._reg_coeff)
self._target = target
self._max_quad_constraint_val = constraint_value_1
self._max_lin_constraint_val = constraint_value_2
self._constraint_name_1 = constraint_name_1
self._constraint_name_2 = constraint_name_2
self._opt_fun = ext.lazydict(
f_loss=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=loss,
log_name="f_loss",
),
f_grad=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=flat_grad,
log_name="f_grad",
),
f_quad_constraint=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=constraint_term_1,
log_name="quad_constraint",
),
f_lin_constraint=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=constraint_term_2,
log_name="lin_constraint",
),
f_lin_constraint_grad=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=flat_lin_constraint_grad,
log_name="lin_constraint_grad",
),
f_loss_constraint=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=[loss, constraint_term_1, constraint_term_2],
log_name="f_loss_constraint",
),
)
self.last_safe_point = None
self._last_lin_pred_S = 0
self._last_surr_pred_S = 0
def update_opt(self, loss, target, leq_constraint, inputs, extra_inputs=None, constraint_name="constraint", *args,
**kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs, which could be subsampled if needed. It is assumed
that the first dimension of these inputs should correspond to the number of data points
:param extra_inputs: A list of symbolic variables as extra inputs which should not be subsampled
:return: No return value.
"""
inputs = tuple(inputs)
if extra_inputs is None:
extra_inputs = tuple()
else:
extra_inputs = tuple(extra_inputs)
constraint_term, constraint_value = leq_constraint
params = target.get_params(trainable=True)
grads = theano.grad(loss, wrt=params)
flat_grad = ext.flatten_tensor_variables(grads)
constraint_grads = theano.grad(constraint_term, wrt=params)
xs = tuple([ext.new_tensor_like("%s x" % p.name, p) for p in params])
Hx_plain_splits = TT.grad(
TT.sum([TT.sum(g * x) for g, x in itertools.izip(constraint_grads, xs)]),
wrt=params,
)
Hx_plain = TT.concatenate([TT.flatten(s) for s in Hx_plain_splits])
self._target = target
self._max_constraint_val = constraint_value
self._constraint_name = constraint_name
if self._debug_nan:
from theano.compile.nanguardmode import NanGuardMode
mode = NanGuardMode(nan_is_error=True, inf_is_error=True, big_is_error=True)
else:
mode = None
self._opt_fun = ext.lazydict(
f_loss=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=loss,
log_name="f_loss",
mode=mode,
),
f_grad=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=flat_grad,
log_name="f_grad",
mode=mode,
),
f_Hx_plain=lambda: ext.compile_function(
inputs=inputs + extra_inputs + xs,
outputs=Hx_plain,
log_name="f_Hx_plain",
mode=mode,
),
f_constraint=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=constraint_term,
log_name="constraint",
mode=mode,
),
f_loss_constraint=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=[loss, constraint_term],
log_name="f_loss_constraint",
mode=mode,
),
)
def update_opt(self, loss, target, quad_leq_constraint, lin_leq_constraint, inputs,
extra_inputs=None,
constraint_name_1="quad_constraint",
constraint_name_2="lin_constraint",
using_surrogate=False,
true_linear_leq_constraint=None,
precompute=False,
attempt_feasible_recovery=False,
attempt_infeasible_recovery=False,
revert_to_last_safe_point=False,
*args, **kwargs):
self.precompute = precompute
self.attempt_feasible_recovery = attempt_feasible_recovery
self.attempt_infeasible_recovery = attempt_infeasible_recovery
self.revert_to_last_safe_point = revert_to_last_safe_point
inputs = tuple(inputs)
if extra_inputs is None:
extra_inputs = tuple()
else:
extra_inputs = tuple(extra_inputs)
constraint_term_1, constraint_value_1 = quad_leq_constraint
constraint_term_2, constraint_value_2 = lin_leq_constraint
params = target.get_params(trainable=True)
grads = theano.grad(loss, wrt=params, disconnected_inputs='warn')
flat_grad = ext.flatten_tensor_variables(grads)
lin_constraint_grads = theano.grad(constraint_term_2, wrt=params, disconnected_inputs='warn')
flat_lin_constraint_grad = ext.flatten_tensor_variables(lin_constraint_grads)
if using_surrogate and not(precompute):
constraint_term_2 = true_linear_leq_constraint
self._hvp_approach.update_opt(f=constraint_term_1, target=target,
inputs=inputs + extra_inputs,
reg_coeff=self._reg_coeff)
self._target = target
self._max_quad_constraint_val = constraint_value_1
self._max_lin_constraint_val = constraint_value_2
self._constraint_name_1 = constraint_name_1
self._constraint_name_2 = constraint_name_2
self._opt_fun = ext.lazydict(
f_loss=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=loss,
log_name="f_loss",
),
f_grad=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=flat_grad,
log_name="f_grad",
),
f_quad_constraint=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=constraint_term_1,
log_name="quad_constraint",
),
f_lin_constraint=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=constraint_term_2,
log_name="lin_constraint",
),
f_lin_constraint_grad=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=flat_lin_constraint_grad,
log_name="lin_constraint_grad",
),
f_loss_constraint=lambda: ext.compile_function(
inputs=inputs + extra_inputs,
outputs=[loss, constraint_term_1, constraint_term_2],
log_name="f_loss_constraint",
),
)
self.last_safe_point = None
self._last_lin_pred_S = 0
self._last_surr_pred_S = 0
def update_opt(self,
loss,
target,
leq_constraint,
inputs,
constraint_name="constraint",
dummy_loss=None,
*args,
**kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:return: No return value.
"""
constraint_term, constraint_value = leq_constraint
# print("debug101", constraint_term)
with tf.variable_scope(self._name):
penalty_var = tf.placeholder(tf.float32, tuple(), name="penalty")
# temp1 = penalty_var * constraint_term
# temp2 = loss + penalty_var
# temp3 = loss + constraint_term
penalized_loss = loss + penalty_var * constraint_term
self._target = target
self._max_constraint_val = constraint_value
self._constraint_name = constraint_name
self._inputs = inputs
self._loss = loss
self._dummy_loss = dummy_loss
if self._use_momentum_optimizer:
self._adam = tf.train.MomentumOptimizer(learning_rate=0.0000001,
momentum=0.997,
name=self._name)
assert False, "not supported at the moment"
else:
self._adam = tf.train.AdamOptimizer(
name=self._name) #learning_rate=0.001
self._optimizer_vars_initializers = [
var.initializer for var in tf.global_variables()
if self._name in var.name
]
# self._adam = tf.train.MomentumOptimizer(learning_rate=0.001, momentum=0.5)
# self._train_step = self._adam.minimize(self._loss)
if "correction_term" in kwargs:
self._correction_term = kwargs["correction_term"]
else:
self._correction_term = None
self._gradients = self._adam.compute_gradients(loss=self._loss,
var_list=self._target)
# self._gradients = self._adam.compute_gradients(loss=self._loss)
if self._correction_term is None:
self._train_step = self._adam.apply_gradients(self._gradients)
else:
# print("debug1", self._gradients)
# print("debug2", self._correction_term)
# print("debug2", self._correction_term.keys())
self.new_gradients = []
for grad, var in self._gradients:
if var in self._correction_term.keys():
self.new_gradients.append(
(grad + self._correction_term[var], var))
else:
self.new_gradients.append((grad, var))
# print("debug3", self.new_gradients)
self._train_step = self._adam.apply_gradients(self.new_gradients)
# initialize Adam variables
uninit_vars = []
sess = tf.get_default_session()
if sess is None:
sess = tf.Session()
for var in tf.global_variables():
# note - this is hacky, may be better way to do this in newer TF.
try:
sess.run(var)
except tf.errors.FailedPreconditionError:
uninit_vars.append(var)
sess.run(tf.variables_initializer(uninit_vars))
def get_opt_output():
params = target.get_params(trainable=True)
grads = tf.gradients(penalized_loss, params)
for idx, (grad, param) in enumerate(zip(grads, params)):
if grad is None:
grads[idx] = tf.zeros_like(param)
flat_grad = tensor_utils.flatten_tensor_variables(grads)
return [
tf.cast(penalized_loss, tf.float32),
tf.cast(flat_grad, tf.float32),
]
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(
inputs, loss, log_name="f_loss"),
f_constraint=lambda: tensor_utils.compile_function(
inputs, constraint_term, log_name="f_constraint"),
f_penalized_loss=lambda: tensor_utils.compile_function(
inputs=inputs + [penalty_var],
outputs=[penalized_loss, loss, constraint_term],
log_name="f_penalized_loss",
),
f_opt=lambda: tensor_utils.compile_function(
inputs=inputs + [penalty_var],
outputs=get_opt_output(),
))