Policies.klUCBPlus module

The improved kl-UCB policy, for one-parameter exponential distributions. Reference: [Cappé et al. 13](https://arxiv.org/pdf/1210.1136.pdf)

class Policies.klUCBPlus.klUCBPlus(nbArms, tolerance=0.0001, klucb=<function klucbBern>, c=1.0, lower=0.0, amplitude=1.0)

Bases: Policies.klUCB.klUCB

The improved kl-UCB policy, for one-parameter exponential distributions. Reference: [Cappé et al. 13](https://arxiv.org/pdf/1210.1136.pdf)

__str__()

-> str

computeIndex(arm)

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

\[\begin{split}\hat{\mu}_k(t) &= \frac{X_k(t)}{N_k(t)}, \\ U_k(t) &= \sup\limits_{q \in [a, b]} \left\{ q : \mathrm{kl}(\hat{\mu}_k(t), q) \leq \frac{c \log(t / N_k(t))}{N_k(t)} \right\},\\ I_k(t) &= U_k(t).\end{split}\]

If rewards are in \([a, b]\) (default to \([0, 1]\)) and \(\mathrm{kl}(x, y)\) is the Kullback-Leibler divergence between two distributions of means x and y (see Arms.kullback), and c is the parameter (default to 1).

computeAllIndex()

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

__module__ = 'Policies.klUCBPlus'