(self, states)
return tf.reduce_sum(tf.square(target - self.value_function(states)),
                             name='bellmann_error')
Compute the squared bellmann error.

        Parameters
        ----------
        states : array

        Returns
        -------
        error : float
Compute the squared bellmann error.
Compute the squared bellmann error .
def bellmann_error(self, states):
        """Compute the squared bellmann error.

        Parameters
        ----------
        states : array

        Returns
        -------
        error : float
        """
        # Make sure we do not compute the gradient with respect to the
        # training target.
        target = tf.stop_gradient(self.future_values(states))
        # Squared bellmann error
        return tf.reduce_sum(tf.square(target - self.value_function(states)),
                             name='bellmann_error')
def bellmann_error ( self , states ) : # Make sure we do not compute the gradient with respect to the # training target. target = tf . stop_gradient ( self . future_values ( states ) ) # Squared bellmann error return tf . reduce_sum ( tf . square ( target - self . value_function ( states ) ) , name = 'bellmann_error' )