Index of values

A
alpha [Obandit.AlphaUCBParam]	The $ \alpha $ parameter.
alpha [Obandit.AlphaPhiUCBParam]	The $ \alpha $ parameter.
C
c [Obandit.DecayingEpsilonGreedyParam]	The $ c$ hyperparameter.
D
d [Obandit.DecayingEpsilonGreedyParam]	The $ d$ hyperparameter, a tight lower bound on $ \max_{i=1,\cdots,K} \Delta_i $.
E
epsilon [Obandit.EpsilonGreedyParam]	The $ \epsilon $ parameter.
eta [Obandit.FixedExp3Param]	The fixed learning rate $ \eta $.
I
initialBandit [Obandit.RangedBandit]
initialBandit [Obandit.Bandit]	The initial state of the bandit algorithm.
invLFPhi [Obandit.AlphaPhiUCBParam]	The inverse of the Legendre-Fenchel transform of $ \psi $.
K
k [Obandit.HorizonExp3Param]	The number of actions $K$ .
k [Obandit.FixedExp3Param]	The number of actions $ K $ .
k [Obandit.EpsilonGreedyParam]	The number of actions $ K $ .
k [Obandit.DecayingEpsilonGreedyParam]	The number of actions $ K $ .
k [Obandit.RateBanditParam]	The number of actions $K$ .
k [Obandit.KBanditParam]	The number of actions $ K $ .
k [Obandit.AlphaUCBParam]	The number of actions $ K $ .
k [Obandit.AlphaPhiUCBParam]	The number of actions $ K $ .
L
lower [Obandit.RangeParam]	The lower value of the range
N
n [Obandit.HorizonExp3Param]	The $ n $ parameter, the horizon to optimize for.
R
rate [Obandit.RateBanditParam]	The rate.
S
step [Obandit.RangedBandit]
step [Obandit.Bandit]	`step r` advances the bandit game one step, where `r` is the reward for the last action.
U
upper [Obandit.RangeParam]	The upper value of the range