code: def __init__(
      self,
      agent: "ParallelDQNAgent",
      create_env: Callable[[None], "TrainingEnvironment"],
      memory_queue: Queue,
      model_update_queue: Queue,
      done_queue: Queue,
      n_steps: int,
      n_episodes: int,
      eps_end: int = 0.05,
      eps_start: int = 0.99,
      eps_decay_steps: int = 2000,
      update_target_every: int = 2,
  ):
      super().__init__()
  
      self.agent = agent
      self.env = create_env()
      self.memory_queue = memory_queue
      self.model_update_queue = model_update_queue
      self.done_queue = done_queue
      self.n_steps = n_steps or np.iinfo(np.int32).max
      self.n_episodes = n_episodes or np.iinfo(np.int32).max
      self.eps_start = eps_start
      self.eps_end = eps_end
      self.eps_decay_steps = eps_decay_steps
      self.update_target_every = update_target_every
  
      self.env.agent_id = self.agent.id
  
imports: from deprecated import deprecated
  import numpy
  from typing import Callable
  from multiprocessing import Process, Queue
label_desc: Use image node_id 1 to create a new ParallelDQNTrainer object from inherited base classes: Process with example: obj = ParallelDQNTrainer(agent, create_env, memory_queue, model_update_queue, done_queue, n_steps, n_episodes, eps_end, eps_start, eps_decay_steps, update_target_every)