def get_default_action_sampling_strategy():
    """
    If true, then we try each allowed first action at the root of the tree, in which case:
     - we spend as much computing resources to value each board state obtained after a first action,
     - the total number of MC samples may slightly differ from 'num_tirages_MC'.

    Otherwise, we randomly sample the first actions at the root of the tree, in which case:
     - we might over-sample or under-sample a few of them,
     - we have a better control of the total number of MC samples.
    """
    deterministic_sampling_of_actions_at_root = True
    return deterministic_sampling_of_actions_at_root
