class_docstr: Iterable Dataset containing the ExperienceBuffer which will be updated with new experiences during training.
  
  >>> RLDataset(ReplayBuffer(5))  # doctest: +ELLIPSIS
  <...reinforce_learn_Qnet.RLDataset object at ...>
docstr: Args:
      buffer: replay buffer
      sample_size: number of experiences to sample at a time
docstr_tok: ["Args",":","buffer",":","replay","buffer","sample_size",":","number","of","experiences","to","sample","at","a","time"]
code: def __init__(
      self, buffer: ReplayBuffer, sample_size: int = 200
  ) -> None:
      """
      Args:
          buffer: replay buffer
          sample_size: number of experiences to sample at a time
      """
      self.buffer = buffer
      self.sample_size = sample_size
  
imports: import argparse
  from collections import OrderedDict, deque, namedtuple
  from typing import Iterator, List, Tuple
  import gym
  import numpy
  import torch
  import torch.nn
  import torch.optim
  from lightning.pytorch import LightningModule, Trainer, cli_lightning_logo, seed_everything
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
  from torch.utils.data.dataset import IterableDataset
label_desc: Use image node_id 1 to create a new RLDataset object from inherited base classes: IterableDataset with example: obj = RLDataset(buffer, sample_size)