class_docstr: Runner for single-threaded training, based on SequentialVectorEnv.
docstr: Create dummy (sequentially-executed) actors.
docstr_tok: ["Create","dummy","(","sequentially-executed",")","actors","."]
code: def create_distributed_rollout_workers(
      self,
      env_factory: Callable[[], MazeEnv],
      worker_policy: TorchPolicy,
      n_rollout_steps: int,
      n_workers: int,
      batch_size: int,
      rollouts_per_iteration: int,
      split_rollouts_into_transitions: bool,
      env_instance_seeds: List[int],
      replay_buffer: BaseReplayBuffer,
  ) -> DummyDistributedWorkersWithBuffer:
      """Create dummy (sequentially-executed) actors."""
      return DummyDistributedWorkersWithBuffer(
          env_factory=env_factory,
          worker_policy=worker_policy,
          n_rollout_steps=n_rollout_steps,
          n_workers=n_workers,
          batch_size=batch_size,
          rollouts_per_iteration=rollouts_per_iteration,
          split_rollouts_into_transitions=split_rollouts_into_transitions,
          env_instance_seeds=env_instance_seeds,
          replay_buffer=replay_buffer,
      )
  
imports: import copy
  from abc import abstractmethod
  from dataclasses import dataclass
  from typing import Callable, List, Union
  from omegaconf import DictConfig
  from torch.utils.data import Dataset
  from maze.core.agent.policy import Policy
  from maze.core.agent.torch_actor_critic import TorchActorCritic
  from maze.core.agent.torch_policy import TorchPolicy
  from maze.core.agent.torch_state_action_critic import TorchStateActionCritic
  from maze.core.annotations import override
  from maze.core.env.maze_env import MazeEnv
  from maze.core.log_stats.log_stats import LogStatsAggregator, LogStatsLevel, get_stats_logger
  from maze.core.rollout.rollout_generator import RolloutGenerator
  from maze.core.trajectory_recording.datasets.in_memory_dataset import InMemoryDataset
  from maze.core.trajectory_recording.records.trajectory_record import SpacesTrajectoryRecord
  from maze.core.utils.config_utils import SwitchWorkingDirectoryToInput
  from maze.core.utils.factory import Factory
  from maze.train.parallelization.distributed_actors.base_distributed_workers_with_buffer import BaseDistributedWorkersWithBuffer
  from maze.train.parallelization.distributed_actors.dummy_distributed_workers_with_buffer import DummyDistributedWorkersWithBuffer
  from maze.train.parallelization.vector_env.sequential_vector_env import SequentialVectorEnv
  from maze.train.parallelization.vector_env.structured_vector_env import StructuredVectorEnv
  from maze.train.trainers.common.evaluators.rollout_evaluator import RolloutEvaluator
  from maze.train.trainers.common.model_selection.best_model_selection import BestModelSelection
  from maze.train.trainers.common.replay_buffer.replay_buffer import BaseReplayBuffer
  from maze.train.trainers.common.replay_buffer.uniform_replay_buffer import UniformReplayBuffer
  from maze.train.trainers.common.training_runner import TrainingRunner
  from maze.train.trainers.sac.sac_trainer import SAC
  from maze.utils.process import query_cpu
label_desc: Use image node_id 1 for calling the SACDevRunner obj's underlying member method code with example usage: obj.create_distributed_rollout_workers(env_factory, worker_policy, n_rollout_steps, n_workers, batch_size, rollouts_per_iteration, split_rollouts_into_transitions, env_instance_seeds, replay_buffer) and returns: DummyDistributedWorkersWithBuffer