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)

Bases: Policies.IndexPolicy.IndexPolicy

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)

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()

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

__module__ = 'Policies.EmpiricalMeans'