class_docstr: A replay action record policy that executes in each (sub-)step the action stored in the provided action record.
  
  :param action_record_path: Action record or path to action record dump.
  :param with_agent_actions: If True agent actions are returned; else MazeActions.
class_docstr_tok: ["A","replay","action","record","policy","that","executes","in","each","(","sub-",")","step","the","action","stored","in","the","provided","action","record",".",":","param","action_record_path",":","Action","record","or","path","to","action","record","dump",".",":","param","with_agent_actions",":","If","True","agent","actions","are","returned",";","else","MazeActions","."]
docstr: Deterministically returns the action record action at the respective step.
          
docstr_tok: ["Deterministically","returns","the","action","record","action","at","the","respective","step","."]
code: def compute_action(
      self,
      observation: ObservationType,
      maze_state: Optional[MazeStateType],
      env: Optional[MazeEnv],
      actor_id: Optional[ActorID] = None,
      deterministic: bool = False,
  ) -> Union[ActionType, MazeActionType]:
      """Deterministically returns the action record action at the respective step."""
      current_env_time = env.get_env_time()
  
      if self._with_agent_actions:
          if actor_id is None:
              actor_id = env.actor_id()
          return self.action_record.get_agent_action(
              current_env_time, actor_id=actor_id
          )
      else:
          return self.action_record.get_maze_action(current_env_time)
  
imports: import os.path
  from typing import Tuple
  from typing import Union, Optional, Sequence
  from maze.core.agent.policy import Policy
  from maze.core.annotations import override
  from maze.core.env.action_conversion import ActionType
  from maze.core.env.maze_action import MazeActionType
  from maze.core.env.maze_env import MazeEnv
  from maze.core.env.maze_state import MazeStateType
  from maze.core.env.observation_conversion import ObservationType
  from maze.core.env.structured_env import ActorID
  from maze.core.trajectory_recording.records.action_record import ActionRecord
label_desc: Use image node_id 6 for calling the ReplayRecordedActionsPolicy obj's underlying member method code with example usage: obj.compute_action(observation, maze_state, env, actor_id, deterministic) and returns: self, self