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Stochastic Gradient Succeeds for Bandits
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a constant learning rate instead of decaying the learning rate | Stochastic gradient bandit · Stochastic multi-armed bandit | expected suboptimality gap: O(C_{\Delta}/t) | Helped |
| Add an average-reward baseline (ablation) | Stochastic gradient bandit · 10-action stochastic bandit with adversarial initialization | optimal action probability | Hurt |
| Add an action-independent baseline that reduces the effective reward range (ablation) | Stochastic gradient bandit · Stochastic multi-armed bandit | convergence bound constant: R_{\max}^2 -> \bar{R}_{\max}^2 | Helped |
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