Description Details Usage Arguments Methods See Also Examples
Context-free Bernoulli or Binary multi-armed bandit.
Simulates k Bernoulli arms where each arm issues a reward of one with
uniform probability p, and otherwise a reward of zero.
In a bandit scenario, this can be used to simulate a hit or miss event, such as if a user clicks on a headline, ad, or recommended product.
1 |
weightsnumeric vector; probability of reward values for each of the bandit's k arms
new(weights) generates and instantializes a new BasicBernoulliBandit
instance.
get_context(t)argument:
t: integer, time step t.
returns a named list
containing the current d x k dimensional matrix context$X,
the number of arms context$k and the number of features context$d.
get_reward(t, context, action)arguments:
t: integer, time step t.
context: list, containing the current context$X (d x k context matrix),
context$k (number of arms) and context$d (number of context features)
(as set by bandit).
action: list, containing action$choice (as set by policy).
returns a named list containing reward$reward and, where computable,
reward$optimal (used by "oracle" policies and to calculate regret).
Core contextual classes: Bandit, Policy, Simulator,
Agent, History, Plot
Bandit subclass examples: BasicBernoulliBandit, ContextualLogitBandit,
OfflineReplayEvaluatorBandit
Policy subclass examples: EpsilonGreedyPolicy, ContextualLinTSPolicy
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## Not run:
horizon <- 100
sims <- 100
policy <- EpsilonGreedyPolicy$new(epsilon = 0.1)
bandit <- BasicBernoulliBandit$new(weights = c(0.6, 0.1, 0.1))
agent <- Agent$new(policy,bandit)
history <- Simulator$new(agent, horizon, sims)$run()
plot(history, type = "cumulative", regret = TRUE)
## End(Not run)
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