class_docstr: Base Agent class handling the interaction with the environment.
  
  >>> env = gym.make("CartPole-v1")
  >>> buffer = ReplayBuffer(10)
  >>> Agent(env, buffer)  # doctest: +ELLIPSIS
  <...reinforce_learn_Qnet.Agent object at ...>
docstr: Resets the environment and updates the state.
docstr_tok: ["Resets","the","environment","and","updates","the","state","."]
code: def reset(self) -> None:
      """Resets the environment and updates the state."""
      self.state = self.env.reset()
  
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 2 for calling the Agent obj's underlying member method code with example usage: obj.reset() without return types