class_docstr: A decision policy that chooses the action according to agents selected in a circular order.
class_docstr_tok: ["A","decision","policy","that","chooses","the","action","according","to","agents","selected","in","a","circular","order","."]
code: def __init__(self, num_agents: int):
      super().__init__()
      self.round_robin = itertools.cycle(range(num_agents))
  
imports: import copy
  import itertools
  from enum import Enum
  from typing import Union, List, Dict
  import numpy
  from rl_coach.agents.agent_interface import AgentInterface
  from rl_coach.base_parameters import AgentParameters, VisualizationParameters
  from rl_coach.core_types import ActionInfo, EnvResponse, ActionType, RunPhase
  from rl_coach.filters.observation.observation_crop_filter import ObservationCropFilter
  from rl_coach.saver import SaverCollection
  from rl_coach.spaces import ActionSpace
  from rl_coach.spaces import AgentSelection, AttentionActionSpace, SpacesDefinition
  from rl_coach.utils import short_dynamic_import
label_desc: Use image node_id 1 to create a new RoundRobin object from inherited base classes: DecisionPolicy with example: obj = RoundRobin(num_agents)