Four Rooms

Action Space	Discrete(7)
Observation Space	Dict('direction': Discrete(4), 'image': Box(0, 255, (7, 7, 3), uint8), 'mission': MissionSpace(<function FourRoomsEnv._gen_mission at 0x7f813f507160>, None))
Reward Range	(0, 1)
Creation	gymnasium.make("MiniGrid-FourRooms-v0")

Description

Classic four room reinforcement learning environment. The agent must navigate in a maze composed of four rooms interconnected by 4 gaps in the walls. To obtain a reward, the agent must reach the green goal square. Both the agent and the goal square are randomly placed in any of the four rooms.

Mission Space

“reach the goal”

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:

1. The agent reaches the goal.

2. Timeout (see max_steps).

Registered Configurations

• MiniGrid-FourRooms-v0