Door Key#


Action Space	`Discrete(7)`
Observation Space	`Dict('direction': Discrete(4), 'image': Box(0, 255, (7, 7, 3), uint8), 'mission': MissionSpace(<function DoorKeyEnv._gen_mission at 0x7f813f5185e0>, None))`
Reward Range	`(0, 1)`
Creation	`gymnasium.make("MiniGrid-DoorKey-16x16-v0")`

Description#

This environment has a key that the agent must pick up in order to unlock a door 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/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:

The agent reaches the goal.
Timeout (see max_steps).

Registered Configurations#

MiniGrid-DoorKey-5x5-v0
MiniGrid-DoorKey-6x6-v0
MiniGrid-DoorKey-8x8-v0
MiniGrid-DoorKey-16x16-v0