BatchBernoulliBandit

class statrl.settings.bandits.stochastic.batch.envs.parametric.BatchBernoulliBandit(means, batchschedule='constant', name='BMAB-Bernoulli')

Bases: BatchMAB

Batched Bernoulli bandit, built from means and a named batch schedule.

Parameters:

• means (array-like of float or str) – Arm success probabilities, or a key of mean_catalogue.

• batchschedule (str, default='constant') – Key of schedule_catalogue, or "baba,<horizon>".

• name (str, default='BMAB-Bernoulli') – Accepted but unused; see BatchGaussianBandit.

Raises:

KeyError – If batchschedule or a string means names no catalogue entry.

Methods

__init__(means[, batchschedule, name])
close()	Release every attached renderer at the end of a rendered run.
expected_reward(arm)	Mean reward of an arm, for regret accounting only.
get_wrapper_attr(name)	Gets the attribute name from the environment.
has_wrapper_attr(name)	Checks if the attribute name exists in the environment.
render([mode])	Forward the last (arm, reward) pair to every attached renderer.
reset([seed, options])	Start a new run and announce the size of the first batch.
set_wrapper_attr(name, value, *[, force])	Sets the attribute name on the environment with value, see Wrapper.set_wrapper_attr for more info.
step(action)	Pull every arm of one batch and return all their rewards.

Attributes

means	Mean reward of every arm.
metadata
np_random	Returns the environment's internal _np_random that if not set will initialise with a random seed.
np_random_seed	Returns the environment's internal _np_random_seed that if not set will first initialise with a random int as seed.
number_arms	Number of available arms.
optimal_arm	Index of the best arm.
optimal_mean	Mean reward of the best arm, \(\mu^\star\).
render_mode
spec
unwrapped	Returns the base non-wrapped environment.
action_space
observation_space