ContextualPolicy

Contextual bandit policy base class.

Parameters

• reward_obj

  Type → RewardObj | None

  Default → None

  The reward object used to measure the performance of each arm. This can be a metric, a statistic, or a distribution.

• reward_scaler

  Type → compose.TargetTransformRegressor | None

  Default → None

  A reward scaler used to scale the rewards before they are fed to the reward object. This can be useful to scale the rewards to a (0, 1) range for instance.

• burn_in

  Default → 0

  The number of steps to use for the burn-in phase. Each arm is given the chance to be pulled during the burn-in phase. This is useful to mitigate selection bias.

Attributes

• ranking

  Return the list of arms in descending order of performance.

Methods

pull

Pull arm(s).

This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call next(policy.pull(arms)).

Parameters

• arm_ids — 'list[ArmID]'
• context — 'dict | None' — defaults to None

Returns

ArmID: A single arm.

update

Update an arm's state.

Parameters

• arm_id
• context
• reward_args
• reward_kwargs