docstr: Determine if we should start a training phase according to the number of steps passed since the last training
  
  :return:  boolean: True if we should start a training phase
docstr_tok: ["Determine","if","we","should","start","a","training","phase","according","to","the","number","of","steps","passed","since","the","last","training",":","return",":","boolean",":","True","if","we","should","start","a","training","phase"]
code: def _should_train(self):
      """
      Determine if we should start a training phase according to the number of steps passed since the last training
  
      :return:  boolean: True if we should start a training phase
      """
  
      should_update = self._should_update()
  
      steps = self.ap.algorithm.num_consecutive_playing_steps
  
      if should_update:
          if steps.__class__ == EnvironmentEpisodes:
              self.last_training_phase_step = self.current_episode
          if steps.__class__ == EnvironmentSteps:
              self.last_training_phase_step = self.total_steps_counter
  
      return should_update
  
imports: import copy
  import random
  from collections import OrderedDict
  from typing import Dict, List, Union, Tuple
  import numpy
  from six.moves import range
  from rl_coach.agents.agent_interface import AgentInterface
  from rl_coach.architectures.network_wrapper import NetworkWrapper
  from rl_coach.base_parameters import AgentParameters, Device, DeviceType, DistributedTaskParameters, Frameworks
  from rl_coach.core_types import RunPhase, PredictionType, EnvironmentEpisodes, ActionType, Batch, Episode, StateType
  from rl_coach.core_types import Transition, ActionInfo, TrainingSteps, EnvironmentSteps, EnvResponse
  from rl_coach.logger import screen, Logger, EpisodeLogger
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplay
  from rl_coach.saver import SaverCollection
  from rl_coach.spaces import SpacesDefinition, VectorObservationSpace, GoalsSpace, AttentionActionSpace
  from rl_coach.utils import Signal, force_list
  from rl_coach.utils import dynamic_import_and_instantiate_module_from_params
  from rl_coach.memories.backend.memory_impl import get_memory_backend
  from rl_coach.core_types import TimeTypes
  from rl_coach.off_policy_evaluators.ope_manager import OpeManager
  from rl_coach.core_types import PickledReplayBuffer, CsvDataset
label_desc: Use image node_id 23 for calling the Agent obj's underlying member method code with example usage: obj._should_train() and returns: should_update