On the Peril of (Even a Little) Nonstationarity in Satisficing Regret Minimization
On the Peril of (Even a Little) Nonstationarity in Satisficing Regret Minimization explores This paper theoretically analyzes regret minimization in nonstationary bandit problems, showing that even slight nonstationarity significantly impacts performance.. Commercial viability score: 2/10 in Reinforcement Learning.
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