def get_sequence_of_considered_visits(max_num_considered_actions,
                                      num_simulations):
  """Returns a sequence of visit counts considered by Sequential Halving.

  Sequential Halving is a "pure exploration" algorithm for bandits, introduced
  in "Almost Optimal Exploration in Multi-Armed Bandits":
  http://proceedings.mlr.press/v28/karnin13.pdf

  The visit counts allows to implement Sequential Halving by selecting the best
  action from the actions with the currently considered visit count.

  Args:
   max_num_considered_actions: The maximum number of considered actions.
     The `max_num_considered_actions` can be smaller than the number of
     actions.
   num_simulations: The total simulation budget.

  Returns:
    A tuple with visit counts. Length `num_simulations`.
  """
  if max_num_considered_actions <= 1:
    return tuple(range(num_simulations))
  log2max = int(math.ceil(math.log2(max_num_considered_actions)))
  sequence = []
  visits = [0] * max_num_considered_actions
  num_considered = max_num_considered_actions
  while len(sequence) < num_simulations:
    num_extra_visits = max(1, int(num_simulations / (log2max * num_considered)))
    for _ in range(num_extra_visits):
      sequence.extend(visits[:num_considered])
      for i in range(num_considered):
        visits[i] += 1
    # Halving the number of considered actions.
    num_considered = max(2, num_considered // 2)
  return tuple(sequence[:num_simulations])
Returns a sequence of visit counts considered by Sequential Halving.

  Sequential Halving is a "pure exploration" algorithm for bandits, introduced
  in "Almost Optimal Exploration in Multi-Armed Bandits":
  http://proceedings.mlr.press/v28/karnin13.pdf

  The visit counts allows to implement Sequential Halving by selecting the best
  action from the actions with the currently considered visit count.

  Args:
   max_num_considered_actions: The maximum number of considered actions.
     The `max_num_considered_actions` can be smaller than the number of
     actions.
   num_simulations: The total simulation budget.

  Returns:
    A tuple with visit counts. Length `num_simulations`.
  
mctx/_src/seq_halving.py