LinUCBGeneralPolicy {contextual} | R Documentation |
Algorithm 1 LinUCB with unique linear models A Contextual-Bandit Approach to Personalized News Article Recommendation
Lihong Li et all
Each time step t, LinUCBGeneralPolicy
runs a linear regression per arm that produces coefficients for each context feature d
.
It then observes the new context, and generates a predicted payoff or reward together with a confidence interval for each available arm.
It then proceeds to choose the arm with the highest upper confidence bound.
policy <- LinUCBGeneralPolicy(alpha = 1.0)
alpha
double, a positive real value R+; Hyper-parameter adjusting the balance between exploration and exploitation.
name
character string specifying this policy. name
is, among others, saved to the History log and displayed in summaries and plots.
A
d*d identity matrix
b
a zero vector of length d
new(alpha = 1)
Generates a new LinUCBGeneralPolicy
object. Arguments are defined in the Argument section above.
set_parameters()
each policy needs to assign the parameters it wants to keep track of
to list self$theta_to_arms
that has to be defined in set_parameters()
's body.
The parameters defined here can later be accessed by arm index in the following way:
theta[[index_of_arm]]$parameter_name
get_action(context)
here, a policy decides which arm to choose, based on the current values of its parameters and, potentially, the current context.
set_reward(reward, context)
in set_reward(reward, context)
, a policy updates its parameter values
based on the reward received, and, potentially, the current context.
Li, L., Chu, W., Langford, J., & Schapire, R. E. (2010, April). A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on World wide web (pp. 661-670). ACM.
Core contextual classes: Bandit
, Policy
, Simulator
,
Agent
, History
, Plot
Bandit subclass examples: BasicBernoulliBandit
, ContextualLogitBandit
, OfflineReplayEvaluatorBandit
Policy subclass examples: EpsilonGreedyPolicy
, ContextualLinTSPolicy