  We consider a special case of bandit problems, named batched bandits, in
which an agent observes batches of responses over a certain time period. Unlike
previous work, we consider a more practically relevant batch-centric scenario
of batch learning. That is to say, we provide a policy-agnostic regret analysis
and demonstrate upper and lower bounds for the regret of a candidate policy.
Our main theoretical results show that the impact of batch learning is a
multiplicative factor of batch size relative to the regret of online behavior.
Primarily, we study two settings of the stochastic linear bandits: bandits with
finitely and infinitely many arms. While the regret bounds are the same for
both settings, the former setting results hold under milder assumptions. Also,
we provide a more robust result for the 2-armed bandit problem as an important
insight. Finally, we demonstrate the consistency of theoretical results by
conducting empirical experiments and reflect on optimal batch size choice.
