Key Corridor#


Action Space	`Discrete(7)`
Observation Space	`Dict('direction': Discrete(4), 'image': Box(0, 255, (7, 7, 3), uint8), 'mission': MissionSpace(<function KeyCorridorEnv._gen_mission at 0x7f813f5079d0>, [['blue', 'green', 'grey', 'purple', 'red', 'yellow'], ['ball']]))`
Reward Range	`(0, 1)`
Creation	`gymnasium.make("MiniGrid-KeyCorridorS6R3-v0")`

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
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 picks up the correct object.
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