arXiv (OAI Expanded)
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Adapting to Decision-Relevant Non-Stationarity in Decentralized Heterogeneous Bandits
Decentralized bandit systems often contain heterogeneous agents: rewards can change at individual agents even when the best action for the network stays the same. These local changes may cancel when rewards are averaged across agents, so the number of local changes $\Stloc$ can be much larger than t
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