Update linear Thompson Sampling model. — update

Updates the parameters of a linear Thompson Sampling model for multi-armed bandit problems based on new observations.

Usage

update_thompson(ws, yobs, model, xs = NULL, ps = NULL, balanced = NULL)

Arguments

ws: Integer vector. Indicates which arm was chosen for observations at each time t. Length A, where A is the number of observations. Must not contain NA values.
yobs: Numeric vector. Observed outcomes, length A. Must not contain NA values.
model: List. Contains the parameters of the LinTSModel.
xs: Optional matrix. Covariates of shape [A, p], where p is the number of features, if the LinTSModel is contextual. Default is NULL. Must not contain NA values.
ps: Optional matrix. Probabilities of selecting each arm for each observation, if the LinTSModel is balanced. Default is NULL.
balanced: Logical. Indicates whether to use balanced Thompson Sampling. Default is NULL.

Value

A list containing the updated parameters of the LinTSModel.

Examples

set.seed(123)
model <- LinTSModel(K = 5, p = 3, floor_start = 1, floor_decay = 0.9, num_mc = 100,
                    is_contextual = TRUE)
A <- 1000
ws <- numeric(A)
yobs <- numeric(A)
model <- update_thompson(ws = ws, yobs = yobs, model = model)