  The contribution focuses on the problem of exploration within the task of
knowledge transfer. Knowledge transfer refers to the useful application of the
knowledge gained while learning the source task in the target task. The
intended benefit of knowledge transfer is to speed up the learning process of
the target task. The article aims to compare several exploration methods used
within a deep transfer learning algorithm, particularly Deep Target Transfer
$Q$-learning. The methods used are $\epsilon$-greedy, Boltzmann, and upper
confidence bound exploration. The aforementioned transfer learning algorithms
and exploration methods were tested on the virtual drone problem. The results
have shown that the upper confidence bound algorithm performs the best out of
these options. Its sustainability to other applications is to be checked.
