# Policies.EmpiricalMeans module¶

The naive Empirical Means policy for bounded bandits: like UCB but without a bias correction term. Note that it is equal to UCBalpha with alpha=0, only quicker.

class Policies.EmpiricalMeans.EmpiricalMeans(nbArms, lower=0.0, amplitude=1.0)[source]

The naive Empirical Means policy for bounded bandits: like UCB but without a bias correction term. Note that it is equal to UCBalpha with alpha=0, only quicker.

computeIndex(arm)[source]

Compute the current index, at time t and after $$N_k(t)$$ pulls of arm k:

$I_k(t) = \frac{X_k(t)}{N_k(t)}.$
computeAllIndex()[source]

Compute the current indexes for all arms, in a vectorized manner.

__module__ = 'Policies.EmpiricalMeans'