tensorforce/core/optimizers/kfac.py
def tf_step(self, time, variables, **kwargs):
        """
        Creates the TensorFlow operations for performing an optimization step on the given variables, including
        actually changing the values of the variables.

        Args:
            time: Time tensor. Not used for this optimizer.
            variables: List of variables to optimize.
            **kwargs: 
                fn_loss : loss function tensor to differentiate.

        Returns:
            List of delta tensors corresponding to the updates for each optimized variable.
        """
        fn_loss = kwargs["fn_loss"]
        if variables is None:
            variables = tf.trainable_variables
        return tf.gradients(fn_loss, variables)
def tf_step(self, time, variables, **kwargs):
        """
        Creates the TensorFlow operations for performing an optimization step on the given variables, including
        actually changing the values of the variables.

        Args:
            time: Time tensor. Not used for this optimizer.
            variables: List of variables to optimize.
            **kwargs: 
                fn_loss : loss function tensor to differentiate.

        Returns:
            List of delta tensors corresponding to the updates for each optimized variable.
        """
        fn_loss = kwargs["fn_loss"]
        if variables is None:
            variables = tf.trainable_variables
        return tf.gradients(fn_loss, variables)
def tf_step ( self , time , variables , * * kwargs ) : fn_loss = kwargs [ "fn_loss" ] if variables is None : variables = tf . trainable_variables return tf . gradients ( fn_loss , variables )
Creates the TensorFlow operations for performing an optimization step on the given variables, including
        actually changing the values of the variables.

        Args:
            time: Time tensor. Not used for this optimizer.
            variables: List of variables to optimize.
            **kwargs: 
                fn_loss : loss function tensor to differentiate.

        Returns:
            List of delta tensors corresponding to the updates for each optimized variable.
Creates the TensorFlow operations for performing an optimization step on the given variables including actually changing the values of the variables .