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Bandit policy base class.


  • reward_obj (Union[river.stats.base.Statistic, river.metrics.base.Metric, river.proba.base.Distribution]) – defaults to None

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

  • burn_in – defaults to 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.


  • ranking

    Return the list of arms in descending order of performance.



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 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)).


  • arm_ids (List[Union[int, str]])

Update an arm's state.


  • arm_id
  • reward_args
  • reward_kwargs