  A survey is performed of various Multi-Armed Bandit (MAB) strategies in order
to examine their performance in circumstances exhibiting non-stationary
stochastic reward functions in conjunction with delayed feedback. We run
several MAB simulations to simulate an online eCommerce platform for grocery
pick up, optimizing for product availability. In this work, we evaluate several
popular MAB strategies, such as $\epsilon$-greedy, UCB1, and Thompson Sampling.
We compare the respective performances of each MAB strategy in the context of
regret minimization. We run the analysis in the scenario where the reward
function is non-stationary. Furthermore, the process experiences delayed
feedback, where the reward function is not immediately responsive to the arm
played. We devise a new adaptive technique (AG1) tailored for non-stationary
reward functions in the delayed feedback scenario. The results of the
simulation show show superior performance in the context of regret minimization
compared to traditional MAB strategies.
