class_docstr: The ParameterNoise exploration policy is intended for both discrete and continuous action spaces.
  It applies the exploration policy by replacing all the dense network layers with noisy layers.
  The noisy layers have both weight means and weight standard deviations, and for each forward pass of the network
  the weights are sampled from a normal distribution that follows the learned weights mean and standard deviation
  values.
  
  Warning: currently supported only by DQN variants
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docstr: :param action_space: the action space used by the environment
docstr_tok: [":","param","action_space",":","the","action","space","used","by","the","environment"]
code: def __init__(
      self,
      network_params: Dict[str, NetworkParameters],
      action_space: ActionSpace,
  ):
      """
      :param action_space: the action space used by the environment
      """
      super().__init__(action_space)
      self.network_params = network_params
      self._replace_network_dense_layers()
  
file_path: coach/rl_coach/exploration_policies/parameter_noise.py
imports: from typing import List, Dict
  import numpy
  from rl_coach.architectures.layers import NoisyNetDense
  from rl_coach.base_parameters import AgentParameters, NetworkParameters
  from rl_coach.spaces import ActionSpace, BoxActionSpace, DiscreteActionSpace
  from rl_coach.core_types import ActionType
  from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy, ExplorationParameters
label_desc: Use image node_id 1 to create a new ParameterNoise object from inherited base classes: ExplorationPolicy with example: obj = ParameterNoise(network_params, action_space)