  The quality of data driven learning algorithms scales significantly with the
quality of data available. One of the most straight-forward ways to generate
good data is to sample or explore the data source intelligently. Smart sampling
can reduce the cost of gaining samples, reduce computation cost in learning,
and enable the learning algorithm to adapt to unforeseen events. In this paper,
we teach three Deep Q-Networks (DQN) with different exploration strategies to
solve a problem of puncturing ongoing transmissions for URLLC messages. We
demonstrate the efficiency of two adaptive exploration candidates,
variance-based and Maximum Entropy-based exploration, compared to the standard,
simple epsilon-greedy exploration approach.
