class_docstr: Add Gaussian noise to the action taken by the deterministic policy.
  
  Args:
      env_spec (EnvSpec): Environment spec to explore.
      policy (garage.Policy): Policy to wrap.
      total_timesteps (int): Total steps in the training, equivalent to
          max_episode_length * n_epochs.
      max_sigma (float): Action noise standard deviation at the start of
          exploration.
      min_sigma (float): Action noise standard deviation at the end of the
          decay period.
      decay_ratio (float): Fraction of total steps for epsilon decay.
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docstr: Get actions from this policy for the input observation.
  
  Args:
      observations(list): Observations from the environment.
  
  Returns:
      np.ndarray: Actions with noise.
      List[dict]: Arbitrary policy state information (agent_info).
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code: def get_actions(self, observations):
      """Get actions from this policy for the input observation.
  
      Args:
          observations(list): Observations from the environment.
  
      Returns:
          np.ndarray: Actions with noise.
          List[dict]: Arbitrary policy state information (agent_info).
  
      """
      actions, agent_infos = self.policy.get_actions(observations)
      for itr, _ in enumerate(actions):
          actions[itr] = np.clip(
              actions[itr]
              + np.random.normal(size=actions[itr].shape)
              * self._sigma(),
              self._action_space.low,
              self._action_space.high,
          )
          self._total_env_steps += 1
      return actions, agent_infos
  
file_path: garage/src/garage/np/exploration_policies/add_gaussian_noise.py
imports: import akro
  from dowel import tabular
  import numpy
  from garage.np.exploration_policies.exploration_policy import ExplorationPolicy
label_desc: Use image node_id 3 for calling the AddGaussianNoise obj's underlying member method code with example usage: obj.get_actions(observations) and returns: actions,  agent_infos