code: def __init__(self):
      # Architecture parameters
      self.use_accumulated_reward_as_measurement = False
  
      # Agent parameters
      self.num_consecutive_playing_steps = EnvironmentSteps(1)
      self.num_consecutive_training_steps = (
          1  # TODO: update this to TrainingSteps
      )
  
      self.heatup_using_network_decisions = False
      self.discount = 0.99
      self.apply_gradients_every_x_episodes = 5
      self.num_steps_between_copying_online_weights_to_target = (
          TrainingSteps(0)
      )
      self.rate_for_copying_weights_to_target = 1.0
      self.load_memory_from_file_path = None
      self.store_transitions_only_when_episodes_are_terminated = False
  
      # HRL / HER related params
      self.in_action_space = None
  
      # distributed agents params
      self.share_statistics_between_workers = True
  
      # n-step returns
      self.n_step = (
          -1
      )  # calculate the total return (no bootstrap, by default)
  
      # Distributed Coach params
      self.distributed_coach_synchronization_type = None
  
      # Should the workers wait for full episode
      self.act_for_full_episodes = False
  
      # Support for parameter noise
      self.supports_parameter_noise = False
  
      # Override, in retrospective, all the episode rewards with the last reward in the episode
      # (sometimes useful for sparse, end of the episode, rewards problems)
      self.override_episode_rewards_with_the_last_transition_reward = (
          False
      )
  
      # Filters - TODO consider creating a FilterParameters class and initialize the filters with it
      self.update_pre_network_filters_state_on_train = False
      self.update_pre_network_filters_state_on_inference = True
  
imports: import inspect
  import json
  import os
  import sys
  import types
  from collections import OrderedDict
  from enum import Enum
  from typing import Dict, List, Union
  from rl_coach.core_types import TrainingSteps, EnvironmentSteps, GradientClippingMethod, RunPhase, SelectedPhaseOnlyDumpFilter, MaxDumpFilter
  from rl_coach.filters.filter import NoInputFilter
  from rl_coach.logger import screen
label_desc: Use image node_id 1 to create a new AlgorithmParameters object from inherited base classes: Parameters with example: obj = AlgorithmParameters()