Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use reward-biased maximum likelihood for exploration | Linear contextual bandits · Synthetic settings with static and time-varying contexts | cumulative regret: 1.86 vs 0.90 | Mixed |
| Use reward-biased maximum likelihood for exploration | Linear contextual bandits · Synthetic static contexts, Figure (a), 50 trials | standard deviation of cumulative regret: 0.42 vs 74.66 | Helped |
| Use the closed-form LinRBMLE index | Linear contextual bandits · Synthetic static contexts, d = 100, K = 100 | computation time per decision: 0.127 vs 0.149 | Helped |
| Choose the reward-bias schedule α(t)=√t | LinRBMLE · Standard linear bandits | cumulative regret bound: O(√T log T) | Helped |
| Extend reward-biased maximum likelihood to generalized linear models | GLM-RBMLE · Generalized linear bandits | cumulative regret bound: O(√T log T) | Helped |
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