from __future__ import annotations from minigrid.core.constants import COLOR_NAMES from minigrid.core.mission import MissionSpace from minigrid.core.roomgrid import RoomGrid class KeyCorridorEnv(RoomGrid): """ ## Description This environment is similar to the locked room environment, but there are multiple registered environment configurations of increasing size, making it easier to use curriculum learning to train an agent to solve it. The agent has to pick up an object which is behind a locked door. The key is hidden in another room, and the agent has to explore the environment to find it. The mission string does not give the agent any clues as to where the key is placed. This environment can be solved without relying on language. ## Mission Space "pick up the {color} {obj_type}" {color} is the color of the object. Can be "red", "green", "blue", "purple", "yellow" or "grey". {type} is the type of the object. Can be "ball" or "key". ## Action Space | Num | Name | Action | |-----|--------------|-------------------| | 0 | left | Turn left | | 1 | right | Turn right | | 2 | forward | Move forward | | 3 | pickup | Pick up an object | | 4 | drop | Unused | | 5 | toggle | Unused | | 6 | done | Unused | ## Observation Encoding - Each tile is encoded as a 3 dimensional tuple: `(OBJECT_IDX, COLOR_IDX, STATE)` - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in [minigrid/core/constants.py](minigrid/core/constants.py) - `STATE` refers to the door state with 0=open, 1=closed and 2=locked ## Rewards A reward of '1 - 0.9 * (step_count / max_steps)' is given for success, and '0' for failure. ## Termination The episode ends if any one of the following conditions is met: 1. The agent picks up the correct object. 2. Timeout (see `max_steps`). ## Registered Configurations S: room size. R: Number of rows. - `MiniGrid-KeyCorridorS3R1-v0` - `MiniGrid-KeyCorridorS3R2-v0` - `MiniGrid-KeyCorridorS3R3-v0` - `MiniGrid-KeyCorridorS4R3-v0` - `MiniGrid-KeyCorridorS5R3-v0` - `MiniGrid-KeyCorridorS6R3-v0` """ def __init__( self, num_rows=3, obj_type="ball", room_size=6, max_steps: int | None = None, **kwargs, ): self.obj_type = obj_type mission_space = MissionSpace( mission_func=self._gen_mission, ordered_placeholders=[COLOR_NAMES, [obj_type]], ) if max_steps is None: max_steps = 30 * room_size**2 super().__init__( mission_space=mission_space, room_size=room_size, num_rows=num_rows, max_steps=max_steps, **kwargs, ) @staticmethod def _gen_mission(color: str, obj_type: str): return f"pick up the {color} {obj_type}" def _gen_grid(self, width, height): super()._gen_grid(width, height) # Connect the middle column rooms into a hallway for j in range(1, self.num_rows): self.remove_wall(1, j, 3) # Add a locked door on the bottom right # Add an object behind the locked door room_idx = self._rand_int(0, self.num_rows) door, _ = self.add_door(2, room_idx, 2, locked=True) obj, _ = self.add_object(2, room_idx, kind=self.obj_type) # Add a key in a random room on the left side self.add_object(0, self._rand_int(0, self.num_rows), "key", door.color) # Place the agent in the middle self.place_agent(1, self.num_rows // 2) # Make sure all rooms are accessible self.connect_all() self.obj = obj self.mission = f"pick up the {obj.color} {obj.type}" def step(self, action): obs, reward, terminated, truncated, info = super().step(action) if action == self.actions.pickup: if self.carrying and self.carrying == self.obj: reward = self._reward() terminated = True return obs, reward, terminated, truncated, info