Empty#


Action Space	`Discrete(7)`
Observation Space	`Dict('direction': Discrete(4), 'image': Box(0, 255, (7, 7, 3), uint8), 'mission': MissionSpace(<function EmptyEnv._gen_mission at 0x7f813f518b80>, None))`
Reward Range	`(0, 1)`
Creation	`gymnasium.make("MiniGrid-Empty-16x16-v0")`

Description#

This environment is an empty room, and the goal of the agent is to reach the green goal square, which provides a sparse reward. A small penalty is subtracted for the number of steps to reach the goal. This environment is useful, with small rooms, to validate that your RL algorithm works correctly, and with large rooms to experiment with sparse rewards and exploration. The random variants of the environment have the agent starting at a random position for each episode, while the regular variants have the agent always starting in the corner opposite to the goal.

Mission Space#

“get to the green goal square”

Action Space#

Num	Name	Action
0	left	Turn left
1	right	Turn right
2	forward	Move forward
3	pickup	Unused
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 reaches the goal.
Timeout (see max_steps).

Registered Configurations#

MiniGrid-Empty-5x5-v0
MiniGrid-Empty-Random-5x5-v0
MiniGrid-Empty-6x6-v0
MiniGrid-Empty-Random-6x6-v0
MiniGrid-Empty-8x8-v0
MiniGrid-Empty-16x16-v0