def tf_step(
        self,
        time,
        variables,
        arguments,
        fn_loss,
        fn_reference,
        **kwargs
    ):
        """
        Creates the TensorFlow operations for performing an optimization step.

        Args:
            time: Time tensor.
            variables: List of variables to optimize.
            arguments: Dict of arguments for callables, like fn_loss.
            fn_loss: A callable returning the loss of the current model.
            fn_reference: A callable returning the reference values, in case of a comparative loss.
            **kwargs: Additional arguments passed on to the internal optimizer.

        Returns:
            List of delta tensors corresponding to the updates for each optimized variable.
        """

        # Set reference to compare with at each optimization step, in case of a comparative loss.
        arguments['reference'] = fn_reference(**arguments)

        # Negative value since line search maximizes.
        loss_before = -fn_loss(**arguments)

        with tf.control_dependencies(control_inputs=(loss_before,)):
            deltas = self.optimizer.step(
                time=time,
                variables=variables,
                arguments=arguments,
                fn_loss=fn_loss,
                return_estimated_improvement=True,
                **kwargs
            )

            if isinstance(deltas, tuple):
                # If 'return_estimated_improvement' argument exists.
                if len(deltas) != 2:
                    raise TensorForceError("Unexpected output of internal optimizer.")
                deltas, estimated_improvement = deltas
                # Negative value since line search maximizes.
                estimated_improvement = -estimated_improvement
            else:
                estimated_improvement = None

        with tf.control_dependencies(control_inputs=deltas):
                # Negative value since line search maximizes.
            loss_step = -fn_loss(**arguments)

        with tf.control_dependencies(control_inputs=(loss_step,)):

            def evaluate_step(deltas):
                with tf.control_dependencies(control_inputs=deltas):
                    applied = self.apply_step(variables=variables, deltas=deltas)
                with tf.control_dependencies(control_inputs=(applied,)):
                    # Negative value since line search maximizes.
                    return -fn_loss(**arguments)

            return self.solver.solve(
                fn_x=evaluate_step,
                x_init=deltas,
                base_value=loss_before,
                target_value=loss_step,
                estimated_improvement=estimated_improvement
            )
def tf_step(
        self,
        time,
        variables,
        arguments,
        fn_loss,
        fn_reference,
        **kwargs
    ):
        

        
        arguments['reference'] = fn_reference(**arguments)

        
        loss_before = -fn_loss(**arguments)

        with tf.control_dependencies(control_inputs=(loss_before,)):
            deltas = self.optimizer.step(
                time=time,
                variables=variables,
                arguments=arguments,
                fn_loss=fn_loss,
                return_estimated_improvement=True,
                **kwargs
            )

            if isinstance(deltas, tuple):
                
                if len(deltas) != 2:
                    raise TensorForceError("Unexpected output of internal optimizer.")
                deltas, estimated_improvement = deltas
                
                estimated_improvement = -estimated_improvement
            else:
                estimated_improvement = None

        with tf.control_dependencies(control_inputs=deltas):
                
            loss_step = -fn_loss(**arguments)

        with tf.control_dependencies(control_inputs=(loss_step,)):

            def evaluate_step(deltas):
                with tf.control_dependencies(control_inputs=deltas):
                    applied = self.apply_step(variables=variables, deltas=deltas)
                with tf.control_dependencies(control_inputs=(applied,)):
                    
                    return -fn_loss(**arguments)

            return self.solver.solve(
                fn_x=evaluate_step,
                x_init=deltas,
                base_value=loss_before,
                target_value=loss_step,
                estimated_improvement=estimated_improvement
            )
def tf_step ( self , time , variables , arguments , fn_loss , fn_reference , * * kwargs ) : # Set reference to compare with at each optimization step, in case of a comparative loss. arguments [ 'reference' ] = fn_reference ( * * arguments ) # Negative value since line search maximizes. loss_before = - fn_loss ( * * arguments ) with tf . control_dependencies ( control_inputs = ( loss_before , ) ) : deltas = self . optimizer . step ( time = time , variables = variables , arguments = arguments , fn_loss = fn_loss , return_estimated_improvement = True , * * kwargs ) if isinstance ( deltas , tuple ) : # If 'return_estimated_improvement' argument exists. if len ( deltas ) != 2 : raise TensorForceError ( "Unexpected output of internal optimizer." ) deltas , estimated_improvement = deltas # Negative value since line search maximizes. estimated_improvement = - estimated_improvement else : estimated_improvement = None with tf . control_dependencies ( control_inputs = deltas ) : # Negative value since line search maximizes. loss_step = - fn_loss ( * * arguments ) with tf . control_dependencies ( control_inputs = ( loss_step , ) ) : def evaluate_step ( deltas ) : with tf . control_dependencies ( control_inputs = deltas ) : applied = self . apply_step ( variables = variables , deltas = deltas ) with tf . control_dependencies ( control_inputs = ( applied , ) ) : # Negative value since line search maximizes. return - fn_loss ( * * arguments ) return self . solver . solve ( fn_x = evaluate_step , x_init = deltas , base_value = loss_before , target_value = loss_step , estimated_improvement = estimated_improvement )
Creates the TensorFlow operations for performing an optimization step.

        Args:
            time: Time tensor.
            variables: List of variables to optimize.
            arguments: Dict of arguments for callables, like fn_loss.
            fn_loss: A callable returning the loss of the current model.
            fn_reference: A callable returning the reference values, in case of a comparative loss.
            **kwargs: Additional arguments passed on to the internal optimizer.

        Returns:
            List of delta tensors corresponding to the updates for each optimized variable.
Creates the TensorFlow operations for performing an optimization step .