docstr: Write an episode summary line to the terminal
  
  :return: None
docstr_tok: ["Write","an","episode","summary","line","to","the","terminal",":","return",":","None"]
code: def log_to_screen(self) -> None:
      """
      Write an episode summary line to the terminal
  
      :return: None
      """
      # log to screen
      log = OrderedDict()
      log["Name"] = self.full_name_id
      if self.task_id is not None:
          log["Worker"] = self.task_id
      log["Episode"] = self.current_episode
      log["Total reward"] = np.round(
          self.total_reward_in_current_episode, 2
      )
      log["Exploration"] = np.round(
          self.exploration_policy.get_control_param(), 2
      )
      log["Steps"] = self.total_steps_counter
      log["Training iteration"] = self.training_iteration
      screen.log_dict(log, prefix=self.phase.value)
  
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 16 for calling the Agent obj's underlying member method code with example usage: obj.log_to_screen() without return types