JuniorTheoryCommon

What does this program print?

Python

import numpy as np

probs = np.array([0.3, 0.5, 0.8])
pulls = np.array([2, 0, 2, 1, 2])
regret = (probs.max() - probs[pulls]).sum()
print(round(regret, 2))

0.8

Expecting 3.2, the summed probabilities of the arms pulled: regret sums the gaps to the best arm, 0.8 minus each pulled arm's probability, so the three pulls of arm 2 add nothing.
Expecting 0.16, the average gap: regret adds the losses over all steps, 0.5 + 0.3 = 0.8, and it keeps growing while a weaker arm is still being pulled.

How does regret grow over time for a fixed 50/50 split, and for a strategy that stops pulling weak arms?
Why can regret be computed in a simulation but not in a live test?

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JuniorTheoryCommon

What does this program print?

Python

import numpy as np

probs = np.array([0.3, 0.5, 0.8])
pulls = np.array([2, 0, 2, 1, 2])
regret = (probs.max() - probs[pulls]).sum()
print(round(regret, 2))

0.8

Expecting 3.2, the summed probabilities of the arms pulled: regret sums the gaps to the best arm, 0.8 minus each pulled arm's probability, so the three pulls of arm 2 add nothing.
Expecting 0.16, the average gap: regret adds the losses over all steps, 0.5 + 0.3 = 0.8, and it keeps growing while a weaker arm is still being pulled.

How does regret grow over time for a fixed 50/50 split, and for a strategy that stops pulling weak arms?
Why can regret be computed in a simulation but not in a live test?

Back to questions Open topic in roadmap →Answer it yourself — Quiz