def _build_graph(self, **kwargs):
""" Add attributes ph_y, ph_w, ph_lr
methods _compute_loss, _apply_gradients
"""
# build function approximator
cls._build_graph(self, **kwargs)
# build loss function
self.ph_y = tf.placeholder(shape=[None, self.y_dim],
name="y",
dtype=tf_float)
self.ph_w = tf.placeholder(
shape=[None], name='w',
dtype=tf_float) # the weighting for each sample
ts_loss = self._build_loss(self.ts_yh, self.ph_y,
self.ph_w) # user-defined
# build optimizer from loss
ts_grads = list(
zip(U.gradients(ts_loss, self.ts_vars),
self.ts_vars)) # a list of (grad, var) tuples
self.ph_lr = tf.placeholder(shape=[],
name="learning_rate",
dtype=tf_float)
ts_apply_gradients = self._build_apply_gradients(
ts_grads, self.ph_lr)
self._compute_loss = U.function([self.ph_x, self.ph_y, self.ph_w],
ts_loss)
self._apply_gradients = U.function(
[self.ph_x, self.ph_y, self.ph_w, self.ph_lr],
ts_apply_gradients)
def _build_graph(self, **bg_kwargs):
ts_loss, ph_args = self._build_loss_op(**bg_kwargs)
# define compute_loss and compute_grad wrt loss
self._compute_loss = U.function(ph_args, ts_loss)
ts_grads = U.gradients(ts_loss, self._ts_vars)
# fill None with zeros; otherwise tf.run will attempt to fetch for None.
ts_grads = [g if g is not None else tf.zeros_like(v) for (v, g) in
zipsame(self._ts_vars, ts_grads)]
self._compute_grad = U.function(ph_args, ts_grads)
def kl(self, other, x, reversesd=False):
assert type(other) == type(self)
key = str(id(other)) + str(reversesd)
if self._kl_cache[key] is None:
ts_kl = self.build_kl(self, other) if reversesd else self.build_kl(other, self)
_kl = U.function([self.ph_x, other.ph_x], ts_kl)
self._kl_cache[key] = lambda _x: _kl(_x, _x)
return self._kl_cache[key](x)
def _build_graph(self, **kwargs):
""" We treat tfFunctionApproximator as the stochastic map of the policy
(which inputs ph_x and outputs ts_yh) and build additional
attributes/methods required by Policy """
# build tf.Variables
# add attributes ph_x, ts_nor_x, ts_y, _yh, _sh_vars,
# ph_y, ts_pi, ts_logp, ts_pid
tfFunctionApproximator._build_graph(self, **kwargs)
# build additional graphs for Policy
# build conditional distribution
self._pi = self._yh
self._pid = U.function([self.ph_x], self.ts_pid)
self._logp = U.function([self.ph_x, self.ph_y], self.ts_logp)
# build fvp operator (this depends only on self)
ph_g, ts_grads = self._sh_vars.build_flat_ph()
ts_kl = self.build_kl(self, self, p1_sg=True)
ts_kl_grads = U.gradients(ts_kl, self.ts_vars) # grad to the 2nd arg of KL
ts_inner_prod = tf.add_n([tf.reduce_sum(kg * v) for (kg, v) in zipsame(ts_kl_grads, ts_grads)])
ts_fvp = U.gradients(ts_inner_prod, self.ts_vars) # Fisher (information matrix) and Vector Product
ts_fvp = tf.concat([tf.reshape(f, [-1]) for f in ts_fvp], axis=-1) # continuous vector
self._fvp = U.function([self.ph_x, ph_g], ts_fvp)
def _build_graph(self, **kwargs):
"""
Builds the graph of mapping through the user-provided
_build_func_apprx. After all the tf.Variables are created it adds a
new attribute _sh_vars (a Shaper object) for convenient manipulation of
the tf.Variables inside the graph.
Added attributes:
ph_x, ts_nor_x, ts_y, _yh, _sh_vars
"""
# build the input placeholder
self.ph_x = tf.placeholder(shape=[None, self.x_dim],
name="input",
dtype=tf_float)
# build the normalizer for whitening
self.ts_nor_x = self._nor.build_nor_ops(self.ph_x)
# build parameterized function approximator
self.ts_yh = self._build_func_apprx(self.ts_nor_x, **kwargs)
self._yh = U.function([self.ph_x], self.ts_yh)
# build a Shaper of trainable variables for transforming
# between continguous and list representations
self._sh_vars = U.Shaper(self.ts_vars)
def _build_dist(self, ts_nor_x, ph_y):
# mean and std
self.ts_mean = cls._build_func_apprx(self, ts_nor_x) # use the tfFunctionApproximator to define mean
self._ts_logstd = tf.get_variable(
'logstd', shape=[self.y_dim], initializer=tf.constant_initializer(self._init_logstd))
self._ts_stop_std_grad = tf.get_variable('stop_std_grad', initializer=tf.constant(False), trainable=False)
_ts_logstd = tf.cond(self._ts_stop_std_grad, # whether to stop gradient
true_fn=lambda: tf.stop_gradient(self._ts_logstd),
false_fn=lambda: self._ts_logstd)
# make sure the distribution does not degenerate
self.ts_logstd = tf.maximum(tf.to_float(np.log(self._min_std)), _ts_logstd)
ts_std = tf.exp(self.ts_logstd)
self._std = U.function([], ts_std)
self._set_logstd = U.build_set([self._ts_logstd])
self._set_stop_std_grad = U.build_set([self._ts_stop_std_grad])
# pi
self.ts_noise = tf.random_normal(tf.shape(ts_std), stddev=ts_std, seed=self.seed)
ts_pi = self.ts_mean + self.ts_noise
ts_pid = self.ts_mean
# logp
ts_logp = self._build_logp(self.y_dim, ph_y, self.ts_mean, self.ts_logstd)
return ts_pi, ts_logp, ts_pid