  We address how to exploit power control data, gathered from a monitored
environment, for performing power control in an unexplored environment. We
adopt offline deep reinforcement learning, whereby the agent learns the policy
to produce the transmission powers solely by using the data. Experiments
demonstrate that despite discrepancies between the monitored and unexplored
environments, the agent successfully learns the power control very quickly,
even if the objective functions in the monitored and unexplored environments
are dissimilar. About one third of the collected data is sufficient to be of
high-quality and the rest can be from any sub-optimal algorithm.
