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

Multi-Armed Bandit Strategies for Non-Stationary Reward Distributions and Delayed Feedback Processes