docstr: Updates the in-episode log file with all the signal values from the most recent step.
  
  :return: None
docstr_tok: ["Updates","the","in-episode","log","file","with","all","the","signal","values","from","the","most","recent","step",".",":","return",":","None"]
code: def update_step_in_episode_log(self) -> None:
      """
      Updates the in-episode log file with all the signal values from the most recent step.
  
      :return: None
      """
      # log all the signals to file
      self.agent_episode_logger.set_current_time(
          self.current_episode_steps_counter
      )
      self.agent_episode_logger.create_signal_value(
          "Training Iter", self.training_iteration
      )
      self.agent_episode_logger.create_signal_value(
          "In Heatup", int(self._phase == RunPhase.HEATUP)
      )
      self.agent_episode_logger.create_signal_value(
          "ER #Transitions", self.call_memory("num_transitions")
      )
      self.agent_episode_logger.create_signal_value(
          "ER #Episodes", self.call_memory("length")
      )
      self.agent_episode_logger.create_signal_value(
          "Total steps", self.total_steps_counter
      )
      self.agent_episode_logger.create_signal_value(
          "Epsilon", self.exploration_policy.get_control_param()
      )
      self.agent_episode_logger.create_signal_value(
          "Shaped Accumulated Reward",
          self.total_shaped_reward_in_current_episode,
      )
      self.agent_episode_logger.create_signal_value(
          "Update Target Network", 0, overwrite=False
      )
      self.agent_episode_logger.update_wall_clock_time(
          self.current_episode_steps_counter
      )
  
      for signal in self.step_signals:
          self.agent_episode_logger.create_signal_value(
              signal.name, signal.get_last_value()
          )
  
      # dump
      self.agent_episode_logger.dump_output_csv()
  
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 17 for calling the Agent obj's underlying member method code with example usage: obj.update_step_in_episode_log() without return types