class_docstr: Action record holding either maze_action objects of agent actions for later deterministic (seeded) replays.
  
  :param seed: The seed identifying the respective deterministic episode for the action record.
class_docstr_tok: ["Action","record","holding","either","maze_action","objects","of","agent","actions","for","later","deterministic","(","seeded",")","replays",".",":","param","seed",":","The","seed","identifying","the","respective","deterministic","episode","for","the","action","record","."]
docstr: Add action item for specified step.
  
  :param step: The respective time step.
  :param actor_id: ID of the actor to record the action for.
  :param action: The respective agent action.
docstr_tok: ["Add","action","item","for","specified","step",".",":","param","step",":","The","respective","time","step",".",":","param","actor_id",":","ID","of","the","actor","to","record","the","action","for",".",":","param","action",":","The","respective","agent","action","."]
code: def set_agent_action(
      self, step: int, actor_id: ActorID, action: ActionType
  ) -> None:
      """Add action item for specified step.
  
      :param step: The respective time step.
      :param actor_id: ID of the actor to record the action for.
      :param action: The respective agent action.
      """
      self.agent_actions[step][actor_id] = action
  
file_path: maze/maze/core/trajectory_recording/records/action_record.py
imports: import os
  import pickle
  from collections import defaultdict
  from typing import Dict
  from maze.core.env.action_conversion import ActionType
  from maze.core.env.maze_action import MazeActionType
  from maze.core.env.structured_env import ActorID
label_desc: Use image node_id 5 for calling the ActionRecord obj's underlying member method code with example usage: obj.set_agent_action(step, actor_id, action) without return types