  This paper investigates the problem of combinatorial multiarmed bandits with
stochastic submodular (in expectation) rewards and full-bandit delayed
feedback, where the delayed feedback is assumed to be composite and anonymous.
In other words, the delayed feedback is composed of components of rewards from
past actions, with unknown division among the sub-components. Three models of
delayed feedback: bounded adversarial, stochastic independent, and stochastic
conditionally independent are studied, and regret bounds are derived for each
of the delay models. Ignoring the problem dependent parameters, we show that
regret bound for all the delay models is $\tilde{O}(T^{2/3} + T^{1/3} \nu)$ for
time horizon $T$, where $\nu$ is a delay parameter defined differently in the
three cases, thus demonstrating an additive term in regret with delay in all
the three delay models. The considered algorithm is demonstrated to outperform
other full-bandit approaches with delayed composite anonymous feedback.
