class_docstr: Generic flat policy interface.
docstr: Get the top :num_candidates actions as well as the probabilities, q-values, .. leading to the decision.
  
  :param observation: Current observation of the environment
  :param num_candidates: The number of actions that should be returned
  :return: a tuple of sequences, where the first sequence corresponds to the possible actions, the other sequence
           to the associated probabilities
docstr_tok: ["Get","the","top",":","num_candidates","actions","as","well","as","the","probabilities",",","q-values",",","..","leading","to","the","decision",".",":","param","observation",":","Current","observation","of","the","environment",":","param","num_candidates",":","The","number","of","actions","that","should","be","returned",":","return",":","a","tuple","of","sequences",",","where","the","first","sequence","corresponds","to","the","possible","actions",",","the","other","sequence","to","the","associated","probabilities"]
code: def compute_top_action_candidates(
      self, observation: ObservationType, num_candidates: Optional[int]
  ) -> Tuple[Sequence[ActionType], Sequence[float]]:
      """
      Get the top :num_candidates actions as well as the probabilities, q-values, .. leading to the decision.
  
      :param observation: Current observation of the environment
      :param num_candidates: The number of actions that should be returned
      :return: a tuple of sequences, where the first sequence corresponds to the possible actions, the other sequence
               to the associated probabilities
      """
  
file_path: maze/maze/core/agent/flat_policy.py
imports: from abc import ABC, abstractmethod
  from typing import Tuple, Sequence, Optional
  from maze.core.env.action_conversion import ActionType
  from maze.core.env.observation_conversion import ObservationType
label_desc: Use image node_id 2 for calling the FlatPolicy obj's underlying member method code with example usage: obj.compute_top_action_candidates(observation, num_candidates) without return types