---
AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
title: Locked Room
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# Locked Room

### Description

The environment has six rooms, one of which is locked. The agent receives
a textual mission string as input, telling it which room to go to in order
to get the key that opens the locked room. It then has to go into the locked
room in order to reach the final goal. This environment is extremely
difficult to solve with vanilla reinforcement learning alone.

### Mission Space

"get the {lockedroom_color} key from the {keyroom_color} room, unlock the {door_color} door and go to the goal"

{lockedroom_color}, {keyroom_color}, and {door_color} can be "red", "green",
"blue", "purple", "yellow" or "grey".

### 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/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-LockedRoom-v0`