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Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory

AAAI 2020 · Bandits and online learning › Multi-armed bandits · approach: Optimal stochastic bandit algorithms · 4 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use bounded-memory phased exploration with UCB-M on infinite Bernoulli arms instead of non-stationary PolicyQUCB-M (M=2) · I1–I4cumulative regret (/10^5): 3.58 ±0.4 -> 1.84±0.17; 1.11±0.2 -> 0.41±0.02; 1.64 ± 0.2 -> 1.29±0.10; 0.79 ± 0.1 -> 0.49±0.02Helped
Use bounded-memory phased exploration with UCB-M instead of non-recalling √T-runQUCB-M (M=2) · I1, I3, I4cumulative regret (/10^5): 5.35 ±0.5 -> 1.84±0.17; 4.56 ± 0.001 -> 1.29±0.10; 2.55 ± 0.001 -> 0.49±0.02Helped
Use MOSS instead of UCB allocation (ablation)Quantile-regret algorithm · I1–I4, M=2cumulative regret (/10^5): 1.84±0.17 -> 1.74±0.17; 0.41±0.02 -> 0.31±0.02; 1.29±0.10 -> 1.20±0.10; 0.49±0.02 -> 0.39±0.02Helped
Use Thompson Sampling instead of UCB allocation (ablation)Quantile-regret algorithm · I1–I4, M=2cumulative regret (/10^5): 1.84±0.17 -> 1.77±0.17; 0.41±0.02 -> 0.32±0.04; 1.29±0.10 -> 1.23±0.09; 0.49±0.02 -> 0.40±0.02Mixed
Set UCB confidence multiplier η=0.2 (ablation)QUCB-M · I1–I4, M=2cumulative regret (/10^5): 1.84±0.17 -> 2.00±0.20; 0.41±0.02 -> 0.32±0.05; 1.29±0.10 -> 1.41±0.10; 0.49±0.02 -> 0.69±0.04Mixed
Increase memory from M=2 to M=10 (ablation)QUCB-M (η=1) · I1–I4cumulative regret (/10^5): 1.84±0.17 -> 1.98±0.16; 0.41±0.02 -> 0.59±0.02; 1.29±0.10 -> 1.49±0.09; 0.49±0.02 -> 0.63±0.01Hurt
Increase memory from M=2 to M=10 (ablation)QUCB-M (η=0.2) · I1–I4cumulative regret (/10^5): 2.00±0.20 -> 1.71±0.16; 0.32±0.05 -> 0.16±0.02; 1.41±0.10 -> 1.16±0.09; 0.69±0.04 -> {0.30}±0.02Helped
Use M=10 rather than M=2 (ablation)QTS-M · I1–I4cumulative regret (/10^5): 1.77±0.17 -> 1.91±0.16; 0.32±0.04 -> 0.18±0.03; 1.23±0.09 -> 1.14±0.10; 0.40±0.02 -> {0.30}±0.02Mixed
Use M=10 rather than M=2 (ablation)QMoss-M · I1–I4cumulative regret (/10^5): 1.74±0.17 -> 1.69±0.15; 0.31±0.02 -> 0.25±0.02; 1.20±0.10 -> 1.13±0.09; 0.39±0.02 -> {0.30}±0.010Mixed
Use α=0.205 rather than α=0.347 for arm exploration (ablation)QRMUCBM · Infinite Bernoulli instancescumulative regretHurt

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