  This paper addresses the problem of unknown delays in adversarial multi-armed
bandit (MAB) with multiple play. Existing work on similar game setting focused
on only the case where the learner selects an arm in each round. However, there
are lots of applications in robotics where a learner needs to select more than
one arm per round. It is therefore worthwhile to investigate the effect of
delay when multiple arms are chosen. The multiple arms chosen per round in this
setting are such that they experience the same amount of delay. There can be an
aggregation of feedback losses from different combinations of arms selected at
different rounds, and the learner is faced with the challenge of associating
the feedback losses to the arms producing them. To address this problem, this
paper proposes a delayed exponential, exploitation and exploration for multiple
play (DEXP3.M) algorithm. The regret bound is only slightly worse than the
regret of DEXP3 already proposed for the single play setting with unknown
delay.
