ThompsonSamplingPolicy: Policy: Thompson Sampling
In contextual: Simulation and Analysis of Contextual Multi-Armed Bandit Policies

Description Usage Arguments Methods References See Also Examples

ThompsonSamplingPolicy works by maintaining a prior on the the mean rewards of its arms. In this, it follows a beta-binomial model with parameters alpha and beta, sampling values for each arm from its prior and picking the arm with the highest value. When an arm is pulled and a Bernoulli reward is observed, it modifies the prior based on the reward. This procedure is repeated for the next arm pull.

1	policy <- ThompsonSamplingPolicy(alpha = 1, beta = 1)

alpha: integer, a natural number N>0 - first parameter of the Beta distribution
beta: integer, a natural number N>0 - second parameter of the Beta distribution

new(alpha = 1, beta = 1): Generates a new ThompsonSamplingPolicy 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.

Thompson, W. R. (1933). On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika, 25(3/4), 285-294.

Chapelle, O., & Li, L. (2011). An empirical evaluation of thompson sampling. In Advances in neural information processing systems (pp. 2249-2257).

Agrawal, S., & Goyal, N. (2013, February). Thompson sampling for contextual bandits with linear payoffs. In International Conference on Machine Learning (pp. 127-135).b

Core contextual classes: Bandit, Policy, Simulator, Agent, History, Plot

Bandit subclass examples: BasicBernoulliBandit, ContextualLogitBandit, OfflineReplayEvaluatorBandit

Policy subclass examples: EpsilonGreedyPolicy, ContextualLinTSPolicy

horizon            <- 100L
simulations        <- 100L
weights            <- c(0.9, 0.1, 0.1)

policy             <- ThompsonSamplingPolicy$new(alpha = 1, beta = 1)
bandit             <- BasicBernoulliBandit$new(weights = weights)
agent              <- Agent$new(policy, bandit)

history            <- Simulator$new(agent, horizon, simulations, do_parallel = FALSE)$run()

plot(history, type = "cumulative")