tensorforce/models/pg_model.py
def tf_baseline_loss(self, states, internals, reward, update, reference=None):
        """
        Creates the TensorFlow operations for calculating the baseline loss of a batch.

        Args:
            states: Dict of state tensors.
            internals: List of prior internal state tensors.
            reward: Reward tensor.
            update: Boolean tensor indicating whether this call happens during an update.
            reference: Optional reference tensor(s), in case of a comparative loss.

        Returns:
            Loss tensor.
        """
        if self.baseline_mode == 'states':
            loss = self.baseline.loss(
                states=states,
                internals=internals,
                reward=reward,
                update=update,
                reference=reference
            )

        elif self.baseline_mode == 'network':
            loss = self.baseline.loss(
                states=self.network.apply(x=states, internals=internals, update=update),
                internals=internals,
                reward=reward,
                update=update,
                reference=reference
            )

        regularization_loss = self.baseline.regularization_loss()
        if regularization_loss is not None:
            loss += regularization_loss

        return loss
def tf_baseline_loss(self, states, internals, reward, update, reference=None):
        """
        Creates the TensorFlow operations for calculating the baseline loss of a batch.

        Args:
            states: Dict of state tensors.
            internals: List of prior internal state tensors.
            reward: Reward tensor.
            update: Boolean tensor indicating whether this call happens during an update.
            reference: Optional reference tensor(s), in case of a comparative loss.

        Returns:
            Loss tensor.
        """
        if self.baseline_mode == 'states':
            loss = self.baseline.loss(
                states=states,
                internals=internals,
                reward=reward,
                update=update,
                reference=reference
            )

        elif self.baseline_mode == 'network':
            loss = self.baseline.loss(
                states=self.network.apply(x=states, internals=internals, update=update),
                internals=internals,
                reward=reward,
                update=update,
                reference=reference
            )

        regularization_loss = self.baseline.regularization_loss()
        if regularization_loss is not None:
            loss += regularization_loss

        return loss
def tf_baseline_loss ( self , states , internals , reward , update , reference = None ) : if self . baseline_mode == 'states' : loss = self . baseline . loss ( states = states , internals = internals , reward = reward , update = update , reference = reference ) elif self . baseline_mode == 'network' : loss = self . baseline . loss ( states = self . network . apply ( x = states , internals = internals , update = update ) , internals = internals , reward = reward , update = update , reference = reference ) regularization_loss = self . baseline . regularization_loss ( ) if regularization_loss is not None : loss += regularization_loss return loss
Creates the TensorFlow operations for calculating the baseline loss of a batch.

        Args:
            states: Dict of state tensors.
            internals: List of prior internal state tensors.
            reward: Reward tensor.
            update: Boolean tensor indicating whether this call happens during an update.
            reference: Optional reference tensor(s), in case of a comparative loss.

        Returns:
            Loss tensor.
Creates the TensorFlow operations for calculating the baseline loss of a batch .