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AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
title: Four Rooms
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# Four Rooms

### 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/minigrid.py](minigrid/minigrid.py)
- `STATE` refers to the door state with 0=open, 1=closed and 2=locked

### Rewards

A reward of '1' 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`