Incentivized Exploration of Non-Stationary Stochastic Bandits
Sourav Chakraborty Lijun Chen
We study incentivized exploration for the multi-armed bandit (MAB) problem
with non-stationary reward distributions, where players receive compensation
for exploring arms other than the greedy choice and may provide biased feedback
on the reward. We consider two different non-stationary environments:
abruptly-changing and continuously-changing, and propose respective
incentivized exploration algorithms. We show that the proposed algorithms
achieve sublinear regret and compensation over time, thus effectively
incentivizing exploration despite the nonstationarity and the biased or drifted
feedback.