class_docstr: Agent that plays randomly.
class_docstr_tok: ["Agent","that","plays","randomly","."]
code: def __init__(self, env: AnyEnv) -> None:
      self.env = env
      assert isinstance(env.action_space, gym.spaces.Discrete)
      self.num_outputs = env.action_space.n
  
imports: import warnings
  import operator
  import copy
  import typing
  import gym
  import numpy
  from nevergrad.common.tools import pytorch_import_fix
  from nevergrad.parametrization import parameter
  from ..base import ExperimentFunction
  from .None import base
  from .None import envs
  import torch
  import torch.nn.functional
  from torch import nn
  from torch.utils.data import WeightedRandomSampler
label_desc: Use image node_id 1 to create a new RandomAgent object from inherited base classes: base with example: obj = RandomAgent(env)