from __future__ import annotations from minigrid.core.grid import Grid from minigrid.core.mission import MissionSpace from minigrid.core.world_object import Door, Goal, Key from minigrid.minigrid_env import MiniGridEnv class DoorKeyEnv(MiniGridEnv): """ ## Description This environment has a key that the agent must pick up in order to unlock a goal and then get to the green goal square. This environment is difficult, because of the sparse reward, to solve using classical RL algorithms. It is useful to experiment with curiosity or curriculum learning. ## Mission Space "use the key to open the door and then get to the goal" ## 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 | Toggle/activate an object | | 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/minigrid.py](minigrid/minigrid.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 reaches the goal. 2. Timeout (see `max_steps`). ## Registered Configurations - `MiniGrid-DoorKey-5x5-v0` - `MiniGrid-DoorKey-6x6-v0` - `MiniGrid-DoorKey-8x8-v0` - `MiniGrid-DoorKey-16x16-v0` """ def __init__(self, size=8, max_steps: int | None = None, **kwargs): if max_steps is None: max_steps = 10 * size**2 mission_space = MissionSpace(mission_func=self._gen_mission) super().__init__( mission_space=mission_space, grid_size=size, max_steps=max_steps, **kwargs ) @staticmethod def _gen_mission(): return "use the key to open the door and then get to the goal" def _gen_grid(self, width, height): # Create an empty grid self.grid = Grid(width, height) # Generate the surrounding walls self.grid.wall_rect(0, 0, width, height) # Place a goal in the bottom-right corner self.put_obj(Goal(), width - 2, height - 2) # Create a vertical splitting wall splitIdx = self._rand_int(2, width - 2) self.grid.vert_wall(splitIdx, 0) # Place the agent at a random position and orientation # on the left side of the splitting wall self.place_agent(size=(splitIdx, height)) # Place a door in the wall doorIdx = self._rand_int(1, width - 2) self.put_obj(Door("yellow", is_locked=True), splitIdx, doorIdx) # Place a yellow key on the left side self.place_obj(obj=Key("yellow"), top=(0, 0), size=(splitIdx, height)) self.mission = "use the key to open the door and then get to the goal"