RandomPolicy¶
-
class
maze.core.agent.random_policy.
RandomPolicy
(action_spaces_dict: Dict[Union[str, int], gym.spaces.Space])¶ Implements a random structured policy.
- Parameters
action_spaces_dict – The action_spaces dict of the env (will sample from it).
-
compute_action
(observation: Dict[str, numpy.ndarray], maze_state: Optional[Any], env: Optional[maze.core.env.base_env.BaseEnv] = None, actor_id: Optional[maze.core.env.structured_env.ActorID] = None, deterministic: bool = False) → Dict[str, Union[int, numpy.ndarray]]¶ (overrides
Policy
)Sample random action from the given action space.
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compute_top_action_candidates
(observation: Dict[str, numpy.ndarray], num_candidates: Optional[int], maze_state: Optional[Any], env: Optional[maze.core.env.base_env.BaseEnv], actor_id: maze.core.env.structured_env.ActorID = None) → Tuple[Sequence[Dict[str, Union[int, numpy.ndarray]]], Sequence[float]]¶ (overrides
Policy
)Random policy does not provide top action candidates.