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policy_pooled_egreedy.R
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policy_pooled_egreedy.R
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UnpooledEgreedyPolicy <- R6::R6Class(
portable = FALSE,
class = FALSE,
inherit = Policy,
public = list(
class_name = "UnpooledEgreedyPolicy",
epsilon = NULL,
n_subjects = NULL,
initialize = function(epsilon = 0.1, n_subjects = 1) {
super$initialize()
self$epsilon <- epsilon
self$n_subjects <- n_subjects
},
set_parameters = function(context_params) {
self$theta <- list(n = rep(list(as.list(rep(0, context_params$k))), self$n_subjects),
mu = rep(list(as.list(rep(0, context_params$k))), self$n_subjects))
},
get_action = function(t, context) {
if (runif(1) > epsilon) {
action$choice <- which.max(unlist(self$theta$mu[[context$user_context]], FALSE, FALSE))
} else {
action$choice <- sample.int(context$k, 1, replace = TRUE)
}
action
},
set_reward = function(t, context, action, reward) {
arm <- action$choice
user <- context$user_context
reward <- reward$reward
inc(self$theta$n[[user]][[arm]]) <- 1
inc(self$theta$mu[[user]][[arm]]) <- (reward - self$theta$mu[[user]][[arm]]) / self$theta$n[[user]][[arm]]
self$theta
}
)
)
PooledEgreedyPolicy <- R6::R6Class(
portable = FALSE,
class = FALSE,
inherit = Policy,
public = list(
class_name = "PooledEgreedyPolicy",
epsilon = NULL,
n_subjects = NULL,
initialize = function(epsilon = 0.1) {
super$initialize()
self$epsilon <- epsilon
},
set_parameters = function(context_params) {
self$theta_to_arms <- list('N' = 0, 'MU' = 0)
},
get_action = function(t, context) {
if (runif(1) > epsilon) {
action$choice <- which_max_list(self$theta$MU)
} else {
action$choice <- sample.int(context$k, 1, replace = TRUE)
}
action
},
set_reward = function(t, context, action, reward) {
arm <- action$choice
reward <- reward$reward
inc(self$theta$N[[arm]]) <- 1
inc(self$theta$MU[[arm]]) <- (reward - self$theta$MU[[arm]]) / self$theta$N[[arm]]
self$theta
}
)
)
PartiallyPooledEgreedyPolicyOld <- R6::R6Class(
portable = FALSE,
class = FALSE,
inherit = Policy,
public = list(
class_name = "PartiallyPooledEgreedyPolicyOld",
epsilon = NULL,
n_subjects = NULL,
initialize = function(epsilon = 0.1, n_subjects = 1) {
super$initialize()
self$epsilon <- epsilon
self$n_subjects <- n_subjects
},
set_parameters = function(context_params) {
self$theta <- list(n = rep(list(as.list(rep(1, context_params$k))),self$n_subjects),
mu = rep(list(as.list(rep(0, context_params$k))),self$n_subjects))
self$theta_to_arms <- list('N' = 0, 'MU' = 0)
},
get_action = function(t, context) {
user <- context$user_context
if (runif(1) > epsilon) {
betas <- 1 / sqrt(unlist(self$theta$n[[user]]))
gmus <- unlist(self$theta$MU)
umus <- unlist(self$theta$mu[[user]])
p_hat <- betas * gmus + (1 - betas) * umus
action$choice <- which_max_tied(p_hat)
} else {
action$choice <- sample.int(context$k, 1, replace = TRUE)
}
action
},
set_reward = function(t, context, action, reward) {
arm <- action$choice
user <- context$user_context
reward <- reward$reward
inc(self$theta$n[[user]][[arm]]) <- 1
inc(self$theta$mu[[user]][[arm]]) <- (reward - self$theta$mu[[user]][[arm]]) / self$theta$n[[user]][[arm]]
inc(self$theta$N[[arm]]) <- 1
inc(self$theta$MU[[arm]]) <- (reward - self$theta$MU[[arm]]) / self$theta$N[[arm]]
self$theta
}
)
)
PartiallyPooledEgreedyPolicy <- R6::R6Class(
portable = FALSE,
class = FALSE,
inherit = Policy,
public = list(
class_name = "PartiallyPooledEgreedyPolicy",
epsilon = NULL,
n_subjects = NULL,
initialize = function(epsilon = 0.1, n_subjects = 1) {
super$initialize()
self$epsilon <- epsilon
self$n_subjects <- n_subjects
},
set_parameters = function(context_params) {
self$theta <- list(n = rep(list(as.list(rep(1, context_params$k))),self$n_subjects),
mu = rep(list(as.list(rep(0, context_params$k))),self$n_subjects))
self$theta_to_arms <- list('N' = 0, 'MU' = 0)
},
get_action = function(t, context) {
user <- context$user_context
if (runif(1) > epsilon) {
#betas <- 1 / sqrt(unlist(self$theta$n[[user]]))
betas <- 2 / (2 + unlist(self$theta$n[[user]]))
gmus <- unlist(self$theta$MU)
umus <- unlist(self$theta$mu[[user]])
p_hat <- betas * gmus + (1 - betas) * umus
action$choice <- which_max_tied(p_hat)
} else {
action$choice <- sample.int(context$k, 1, replace = TRUE)
}
action
},
set_reward = function(t, context, action, reward) {
arm <- action$choice
user <- context$user_context
reward <- reward$reward
inc(self$theta$n[[user]][[arm]]) <- 1
inc(self$theta$mu[[user]][[arm]]) <- (reward - self$theta$mu[[user]][[arm]]) / self$theta$n[[user]][[arm]]
inc(self$theta$N[[arm]]) <- 1
inc(self$theta$MU[[arm]]) <- (reward - self$theta$MU[[arm]]) / self$theta$N[[arm]]
self$theta
}
)
)
PartiallyBBPooledEgreedyPolicy <- R6::R6Class(
portable = FALSE,
class = FALSE,
inherit = Policy,
public = list(
class_name = "PartiallyPooledBBEgreedyPolicy",
epsilon = NULL,
n_subjects = NULL,
initialize = function(epsilon = 0.1, n_subjects = 1) {
super$initialize()
self$epsilon <- epsilon
self$n_subjects <- n_subjects
},
set_parameters = function(context_params) {
self$theta <- list(n = rep(list(as.list(rep(1, context_params$k))),self$n_subjects),
p = rep(list(as.list(rep(0, context_params$k))),self$n_subjects),
ss = rep(list(as.list(rep(0, context_params$k))),self$n_subjects),
c = rep(list(as.list(rep(0, context_params$k))),self$n_subjects))
self$theta_to_arms <- list("N" = 1, "P" = 0, "SS" = 0, "C" = 0)
},
get_action = function(t, context) {
user <- context$user_context
if (runif(1) > epsilon) {
ns <- self$n_subjects
P <- unlist(self$theta$P)
N <- unlist(self$theta$N)
C <- unlist(self$theta$C)
SS <- unlist(self$theta$SS)
p <- unlist(self$theta$p[[user]])
n <- unlist(self$theta$n[[user]])
sigmasq <- (ns * SS) / ((ns - 1) * N)
M <- pmax((P * (1 - P) - sigmasq) / (sigmasq - ((P * (1 - P)) / ns) * C), 0)
betas <- M / (M + n)
betas[(M==0 | sigmasq==0)] <- 1
p_hat <- betas * P + (1 - betas) * p
action$choice <- which_max_tied(p_hat)
} else {
action$choice <- sample.int(context$k, 1, replace = TRUE)
}
action
},
set_reward = function(t, context, action, reward) {
arm <- action$choice
user <- context$user_context
reward <- reward$reward
inc(self$theta$n[[user]][[arm]]) <- 1
inc(self$theta$p[[user]][[arm]]) <- (reward - self$theta$p[[user]][[arm]]) / self$theta$n[[user]][[arm]]
inc(self$theta$N[[arm]]) <- 1
inc(self$theta$P[[arm]]) <- (reward - self$theta$P[[arm]]) / self$theta$N[[arm]]
self$theta$SS[[arm]] <- self$theta$SS[[arm]] - self$theta$ss[[user]][[arm]] + self$theta$n[[user]][[arm]] *
(self$theta$p[[user]][[arm]] - self$theta$P[[arm]]) ^ 2
self$theta$C[[arm]] <- self$theta$C[[arm]] - self$theta$c[[user]][[arm]] +
1/self$theta$n[[user]][[arm]]
self$theta$c[[user]][[arm]] <- 1 / self$theta$n[[user]][[arm]]
self$theta$ss[[user]][[arm]] <- self$theta$n[[user]][[arm]] * (self$theta$p[[user]][[arm]] - self$theta$P[[arm]]) ^ 2
self$theta
}
)
)