Policies.UCBH module

The UCB-H policy for bounded bandits, with knowing the horizon. Reference: [Audibert et al. 09].

class Policies.UCBH.UCBH(nbArms, horizon=None, alpha=4, lower=0.0, amplitude=1.0)

Bases: Policies.UCBalpha.UCBalpha

The UCB-H policy for bounded bandits, with knowing the horizon. Reference: [Audibert et al. 09].

__init__(nbArms, horizon=None, alpha=4, lower=0.0, amplitude=1.0)

New generic index policy.

• nbArms: the number of arms,

• lower, amplitude: lower value and known amplitude of the rewards.

horizon = None

Parameter \(T\) = known horizon of the experiment.

alpha = None

Parameter alpha

__str__()

-> str

computeIndex(arm)

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

\[I_k(t) = \frac{X_k(t)}{N_k(t)} + \sqrt{\frac{\alpha \log(T)}{2 N_k(t)}}.\]

computeAllIndex()

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

__module__ = 'Policies.UCBH'