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Results tagged with multi-armed-bandits
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user 1641
For questions related to the multi-armed bandit (MAB) problem, in which a fixed limited set of resources must be allocated between competing (alternative) choices in a way that maximizes their expected gain, when each choice's properties are only partially known at the time of allocation.
3
votes
Why is it useful in some applications to use features that are shared by all arms?
Let's first take a look at equation 6:
$$ \mathbf{E} \left[ r_{t,a} \vert \mathbf{x}_{t, a} \right] = \mathbf{z}_{t, a}^{\top} \boldsymbol{\beta}^* + \mathbf{x}_{t, a}^{\top} \boldsymbol{\theta}_a^* …
- 10.5k
3
votes
Accepted
How can I incorporate domain knowledge to choose actions in the case of large action spaces ...
You will want to look into Contextual Multi-Armed Bandits. These are MAB problems that additionally involve feature vectors in some way.
You'll sometimes see researchers considering problems where y …
- 10.5k
1
vote
In UCB, is the actual upper bound an upper bound of an one-sided or two-sided confidence int...
The upper bound used here is derived from Hoeffding's inequality, which provides a symmetric, two-sided confidence interval. A good pair of blog posts on how this bound used in UCB for bandits is deri …
- 10.5k
3
votes
Why aren't exploration techniques, such as UCB or Thompson sampling, used in full RL problems?
Many techniques for the exploration/exploitation dilemma that are inspired by multi-armed bandit problems, such as UCB1, assume that you can explicitly enumerate all state-action pairs; in fact, multi …
- 10.5k