  The performance of taggers for hadronically decaying top quarks and $W$
bosons in $pp$ collisions at $\sqrt{s}$ = 13 TeV recorded by the ATLAS
experiment at the Large Hadron Collider is presented. A set of techniques based
on jet shape observables are studied to determine a set of optimal cut-based
taggers for use in physics analyses. The studies are extended to assess the
utility of combinations of substructure observables as a multivariate tagger
using boosted decision trees or deep neural networks in comparison with taggers
based on two-variable combinations. In addition, for highly boosted top-quark
tagging, a deep neural network based on jet constituent inputs as well as a
re-optimisation of the shower deconstruction technique is presented. The
performance of these taggers is studied in data collected during 2015 and 2016
corresponding to 36.1 fb$^{-1}$ for the $t\bar{t}$ and $\gamma+$jet and 36.7
fb$^{-1}$ for the dijet event topologies.
