All Questions
4 questions
1
vote
1
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174
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Understanding the regret bound of stochastic bandit vs. adversarial bandit
I am a beginner at MAB. One thing that puzzles me these days:
The regret of the UCB policy (and Thompson Sampling with no prior) for stochastic bandit is $\sqrt{KT\ln T}$, but the regret of the EXP3 ...
- 93
3
votes
1
answer
653
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Difference between regret and pseudo-regret definitions
I am following the book Bandit Algorithms. In page 48, they introduces regret after $n$ rounds as
$$
\mathbf{R} = n\mu^\star - \mathbb{E}\Bigg[\sum_{t=1}^n \mathbf{X}_t\Bigg] \tag{1}
$$
In page 55, ...
- 297
4
votes
1
answer
218
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Bandit-like setup but taking max reward over sequential choices
Similar to my other question Bandit-like setup but taking max reward over multiple heads?, I'm interested in situations like the Multi-Armed Bandit setup, except where the reward is aggregated a ...
- 180
3
votes
1
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174
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Bandit-like setting with maximum reward over multiple arms?
If I have a process where I can evaluate one of a number of options per 'round', with variable reward, and I want to maximise reward over time, the multi-armed bandit literature has lots of useful ...
- 180