Policies.Experimentals.UCBwrong module¶
The UCBwrong policy for bounded bandits, like UCB but with a typo on the estimator of means: \(\frac{X_k(t)}{t}\) is used instead of \(\frac{X_k(t)}{N_k(t)}\).
One paper of W.Jouini, C.Moy and J.Palicot from 2009 contained this typo, I reimplemented it just to check that:
its performance is worse than simple UCB,
but not that bad…
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class
Policies.Experimentals.UCBwrong.UCBwrong(nbArms, lower=0.0, amplitude=1.0)[source]¶ Bases:
IndexPolicy.IndexPolicyThe UCBwrong policy for bounded bandits, like UCB but with a typo on the estimator of means.
One paper of W.Jouini, C.Moy and J.Palicot from 2009 contained this typo, I reimplemented it just to check that:
its performance is worse than simple UCB
but not that bad…
-
computeIndex(arm)[source]¶ Compute the current index, at time t and after \(N_k(t)\) pulls of arm k:
\[I_k(t) = \frac{X_k(t)}{t} + \sqrt{\frac{2 \log(t)}{N_k(t)}}.\]
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__module__= 'Policies.Experimentals.UCBwrong'¶