class_docstr: Dummy greedy policy for DummyEnvironment. Recommended action is constant w.r.t. the specified observation.
class_docstr_tok: ["Dummy","greedy","policy","for","DummyEnvironment",".","Recommended","action","is","constant","w.r.t",".","the","specified","observation","."]
docstr: Returns next action to take.
  :return: Action derived from observation state. Constant w.r.t. specified observation.
docstr_tok: ["Returns","next","action","to","take",".",":","return",":","Action","derived","from","observation","state",".","Constant","w.r.t",".","specified","observation","."]
code: def compute_action(
      self,
      observation: ObservationType,
      maze_state: Optional[MazeStateType] = None,
      env: Optional[BaseEnv] = None,
      actor_id: Optional[ActorID] = None,
      deterministic: bool = False,
  ) -> ActionType:
      """
      Returns next action to take.
      :return: Action derived from observation state. Constant w.r.t. specified observation.
      """
  
      # Derive base for action value from specified observation.
      val: float = sum(
          [np.sum(observation[key]) for key in observation]
      )
  
      return {
          "action_0_0": int(val % 10),
          "action_1_0": int(val % 10),
          "action_1_1": np.asarray(
              [round(val - int(val))] * 5, dtype=np.int8
          ),
      }
  
file_path: maze/maze/test/shared_test_utils/dummy_env/agents/dummy_policy.py
imports: from typing import Sequence, Tuple, Optional
  import numpy
  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.base_env import BaseEnv
  from maze.core.env.maze_state import MazeStateType
  from maze.core.env.observation_conversion import ObservationType
  from maze.core.env.structured_env import ActorID
label_desc: Use image node_id 4 for calling the DummyGreedyPolicy obj's underlying member method code with example usage: obj.compute_action(observation, maze_state, env, actor_id, deterministic) and returns: dict