Docs: Fixes to edit button (#259)

docs/.gitignore

@@ -4,3 +4,6 @@ _site
 __pycache__
 _build/
 .vscode/
+
+environments/
+!environments/index.md

docs/conf.py

@@ -84,9 +84,9 @@ html_context: Dict[str, Any] = {}
 html_context["conf_py_path"] = "/docs/"
 html_context["display_github"] = True
 html_context["github_user"] = "Farama-Foundation"
-html_context["github_repo"] = "Gymnasium"
+html_context["github_repo"] = "Minigrid"
 html_context["github_version"] = "master"
-html_context["slug"] = "gymnasium"
+html_context["slug"] = "minigrid"
 
 html_static_path = ["_static"]
 html_css_files = []

docs/environments/blocked_unlock_pickup.md

@@ -1,59 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Blocked Unlock Pickup
----
-
-
-# Blocked Unlock Pickup
-
-### Description
-
-The agent has to pick up a box which is placed in another room, behind a
-locked door. The door is also blocked by a ball which the agent has to move
-before it can unlock the door. Hence, the agent has to learn to move the
-ball, pick up the key, open the door and pick up the object in the other
-room. This environment can be solved without relying on language.
-
-### Mission Space
-
-"pick up the {color} {type}"
-
-{color} is the color of the box. Can be "red", "green", "blue", "purple",
-"yellow" or "grey".
-
-{type} is the type of the object. Can be "box" 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/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 picks up the correct box.
-2. Timeout (see `max_steps`).
-
-### Registered Configurations
-
-- `MiniGrid-BlockedUnlockPickup-v0`

docs/environments/design.md

@@ -1,39 +0,0 @@
----
-layout: "contents"
-title: Design
-firstpage:
----
-
-# General Structure
-
-Structure of the world:
-- The world is an NxM grid of tiles
-- Each tile in the grid world contains zero or one object
-  - Cells that do not contain an object have the value `None`
-- Each object has an associated discrete color (string)
-- Each object has an associated type (string)
-  - Provided object types are: wall, floor, lava, door, key, ball, box and goal
-- The agent can pick up and carry exactly one object (eg: ball or key)
-- To open a locked door, the agent has to be carrying a key matching the door's color
-
-Actions in the basic environment:
-- Turn left
-- Turn right
-- Move forward
-- Pick up an object
-- Drop the object being carried
-- Toggle (open doors, interact with objects)
-- Done (task completed, optional)
-
-Default tile/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
-
-By default, sparse rewards are given for reaching a green goal tile. A
-reward of 1 is given for success, and zero for failure. There is also an
-environment-specific time step limit for completing the task.
-You can define your own reward function by creating a class derived
-from `MiniGridEnv`. Extending the environment with new object types or new actions
-should be very easy. If you wish to do this, you should take a look at the
-[minigrid/minigrid.py](minigrid/minigrid.py) source file.

docs/environments/dist_shift2.md

@@ -1,58 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Dist Shift2
----
-
-
-# Dist Shift2
-
-### Description
-
-This environment is based on one of the DeepMind [AI safety gridworlds]
-(https://github.com/deepmind/ai-safety-gridworlds). The agent starts in the
-top-left corner and must reach the goal which is in the top-right corner,
-but has to avoid stepping into lava on its way. The aim of this environment
-is to test an agent's ability to generalize. There are two slightly
-different variants of the environment, so that the agent can be trained on
-one variant and tested on the other.
-
-### 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/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. The agent falls into lava.
-3. Timeout (see `max_steps`).
-
-### Registered Configurations
-
-- `MiniGrid-DistShift1-v0`
-- `MiniGrid-DistShift2-v0`

docs/environments/door_key.md

@@ -1,56 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Door Key
----
-
-
-# Door Key
-
-### Description
-
-This environment has a key that the agent must pick up in order to unlock a
-goal and then get to the green goal square. This environment is difficult,
-because of the sparse reward, to solve using classical RL algorithms. It is
-useful to experiment with curiosity or curriculum learning.
-
-### Mission Space
-
-"use the key to open the door and then get to the goal"
-
-### 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-DoorKey-5x5-v0`
-- `MiniGrid-DoorKey-6x6-v0`
-- `MiniGrid-DoorKey-8x8-v0`
-- `MiniGrid-DoorKey-16x16-v0`

docs/environments/dynamic.md

@@ -1,62 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Dynamic
----
-
-
-# Dynamic
-
-### Description
-
-This environment is an empty room with moving obstacles.
-The goal of the agent is to reach the green goal square without colliding
-with any obstacle. A large penalty is subtracted if the agent collides with
-an obstacle and the episode finishes. This environment is useful to test
-Dynamic Obstacle Avoidance for mobile robots with Reinforcement Learning in
-Partial Observability.
-
-### 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/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. A '-1' penalty is
-subtracted if the agent collides with an obstacle.
-
-### Termination
-
-The episode ends if any one of the following conditions is met:
-
-1. The agent reaches the goal.
-2. The agent collides with an obstacle.
-3. Timeout (see `max_steps`).
-
-### Registered Configurations
-
-- `MiniGrid-Dynamic-Obstacles-5x5-v0`
-- `MiniGrid-Dynamic-Obstacles-Random-5x5-v0`
-- `MiniGrid-Dynamic-Obstacles-6x6-v0`
-- `MiniGrid-Dynamic-Obstacles-Random-6x6-v0`
-- `MiniGrid-Dynamic-Obstacles-8x8-v0`
-- `MiniGrid-Dynamic-Obstacles-16x16-v0`

docs/environments/empty.md

@@ -1,62 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Empty
----
-
-
-# Empty
-
-### 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/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-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`

docs/environments/fetch.md

@@ -1,66 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Fetch
----
-
-
-# Fetch
-
-### Description
-
-This environment has multiple objects of assorted types and colors. The
-agent receives a textual string as part of its observation telling it which
-object to pick up. Picking up the wrong object terminates the episode with
-zero reward.
-
-### Mission Space
-
-"{syntax} {color} {type}"
-
-{syntax} is one of the following: "get a", "go get a", "fetch a",
-"go fetch a", "you must fetch a".
-
-{color} is the color of the box. Can be "red", "green", "blue", "purple",
-"yellow" or "grey".
-
-{type} is the type of the object. Can be "key" or "ball".
-
-### 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/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 picks up the correct object.
-2. The agent picks up the wrong object.
-2. Timeout (see `max_steps`).
-
-### Registered Configurations
-
-N: number of objects to be generated.
-
-- `MiniGrid-Fetch-5x5-N2-v0`
-- `MiniGrid-Fetch-6x6-N2-v0`
-- `MiniGrid-Fetch-8x8-N3-v0`

docs/environments/four_rooms.md

@@ -1,53 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Four Rooms
----
-
-
-# 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`

docs/environments/go_to_door.md

@@ -1,59 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Go To Door
----
-
-
-# Go To Door
-
-### Description
-
-This environment is a room with four doors, one on each wall. The agent
-receives a textual (mission) string as input, telling it which door to go
-to, (eg: "go to the red door"). It receives a positive reward for performing
-the `done` action next to the correct door, as indicated in the mission
-string.
-
-### Mission Space
-
-"go to the {color} door"
-
-{color} is the color of the door. 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       | Unused               |
-| 4   | drop         | Unused               |
-| 5   | toggle       | Unused               |
-| 6   | done         | Done completing task |
-
-### 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 stands next the correct door performing the `done` action.
-2. Timeout (see `max_steps`).
-
-### Registered Configurations
-
-- `MiniGrid-GoToDoor-5x5-v0`
-- `MiniGrid-GoToDoor-6x6-v0`
-- `MiniGrid-GoToDoor-8x8-v0`

docs/environments/go_to_object.md

@@ -1,10 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Go To Object
----
-
-
-# Go To Object
-
-Environment in which the agent is instructed to go to a given object
-named using an English text string

docs/environments/key_corridor_s6_r3.md

@@ -1,69 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Key Corridor S6 R3
----
-
-
-# Key Corridor S6 R3
-
-### 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/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 picks up the correct object.
-2. 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`

docs/environments/lava_gap_s7.md

@@ -1,60 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Lava Gap S7
----
-
-
-# Lava Gap S7
-
-### Description
-
-The agent has to reach the green goal square at the opposite corner of the
-room, and must pass through a narrow gap in a vertical strip of deadly lava.
-Touching the lava terminate the episode with a zero reward. This environment
-is useful for studying safety and safe exploration.
-
-### Mission Space
-
-Depending on the `obstacle_type` parameter:
-- `Lava`: "avoid the lava and get to the green goal square"
-- otherwise: "find the opening and 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/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. The agent falls into lava.
-3. Timeout (see `max_steps`).
-
-### Registered Configurations
-
-S: size of map SxS.
-
-- `MiniGrid-LavaGapS5-v0`
-- `MiniGrid-LavaGapS6-v0`
-- `MiniGrid-LavaGapS7-v0`

docs/environments/locked_room.md

@@ -1,57 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Locked Room
----
-
-
-# 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`

docs/environments/memory_s7.md

@@ -1,60 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Memory S7
----
-
-
-# Memory S7
-
-### Description
-
-This environment is a memory test. The agent starts in a small room where it
-sees an object. It then has to go through a narrow hallway which ends in a
-split. At each end of the split there is an object, one of which is the same
-as the object in the starting room. The agent has to remember the initial
-object, and go to the matching object at split.
-
-### Mission Space
-
-"go to the matching object at the end of the hallway"
-
-### 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 correct matching object.
-2. The agent reaches the wrong matching object.
-3. Timeout (see `max_steps`).
-
-### Registered Configurations
-
-S: size of map SxS.
-
-- `MiniGrid-MemoryS17Random-v0`
-- `MiniGrid-MemoryS13Random-v0`
-- `MiniGrid-MemoryS13-v0`
-- `MiniGrid-MemoryS11-v0`

docs/environments/multi_room.md

@@ -1,59 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Multi Room
----
-
-
-# Multi Room
-
-### Description
-
-This environment has a series of connected rooms with doors that must be
-opened in order to get to the next room. The final room has the green goal
-square the agent must get to. This environment is extremely difficult to
-solve using RL alone. However, by gradually increasing the number of rooms
-and building a curriculum, the environment can be solved.
-
-### Mission Space
-
-"traverse the rooms to get to 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       | 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
-
-S: size of map SxS.
-N: number of rooms.
-
-- `MiniGrid-MultiRoom-N2-S4-v0` (two small rooms)
-- `MiniGrid-MultiRoom-N4-S5-v0` (four rooms)
-- `MiniGrid-MultiRoom-N6-v0` (six rooms)

docs/environments/obstructed_maze.md

@@ -1,11 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Obstructed Maze
----
-
-
-# Obstructed Maze
-
-A blue ball is hidden in one of the 4 corners of a 3x3 maze. Doors
-are locked, doors are obstructed by a ball and keys are hidden in
-boxes.

docs/environments/playground.md

@@ -1,10 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Playground
----
-
-
-# Playground
-
-Environment with multiple rooms and random objects.
-This environment has no specific goals or rewards.

docs/environments/put_near.md

@@ -1,62 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Put Near
----
-
-
-# Put Near
-
-### Description
-
-The agent is instructed through a textual string to pick up an object and
-place it next to another object. This environment is easy to solve with two
-objects, but difficult to solve with more, as it involves both textual
-understanding and spatial reasoning involving multiple objects.
-
-### Mission Space
-
-"put the {move_color} {move_type} near the {target_color} {target_type}"
-
-{move_color} and {target_color} can be "red", "green", "blue", "purple",
-"yellow" or "grey".
-
-{move_type} and {target_type} Can be "box", "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         | Drop an object    |
-| 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 picks up the wrong object.
-2. The agent drop the correct object near the target.
-3. Timeout (see `max_steps`).
-
-### Registered Configurations
-
-N: number of objects.
-
-- `MiniGrid-PutNear-6x6-N2-v0`
-- `MiniGrid-PutNear-8x8-N3-v0`

docs/environments/red_blue_doors.md

@@ -1,55 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Red Blue Doors
----
-
-
-# Red Blue Doors
-
-### Description
-
-The agent is randomly placed within a room with one red and one blue door
-facing opposite directions. The agent has to open the red door and then open
-the blue door, in that order. Note that, surprisingly, this environment is
-solvable without memory.
-
-### Mission Space
-
-"open the red door then the blue door"
-
-### 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       | 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 opens the blue door having already opened the red door.
-2. The agent opens the blue door without having opened the red door yet.
-3. Timeout (see `max_steps`).
-
-### Registered Configurations
-
-- `MiniGrid-RedBlueDoors-6x6-v0`
-- `MiniGrid-RedBlueDoors-8x8-v0`

docs/environments/simple_crossing_s11_n5.md

@@ -1,76 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Simple Crossing S11 N5
----
-
-
-# Simple Crossing S11 N5
-
-### Description
-
-Depending on the `obstacle_type` parameter:
-- `Lava` - The agent has to reach the green goal square on the other corner
-    of the room while avoiding rivers of deadly lava which terminate the
-    episode in failure. Each lava stream runs across the room either
-    horizontally or vertically, and has a single crossing point which can be
-    safely used; Luckily, a path to the goal is guaranteed to exist. This
-    environment is useful for studying safety and safe exploration.
-- otherwise - Similar to the `LavaCrossing` environment, the agent has to
-    reach the green goal square on the other corner of the room, however
-    lava is replaced by walls. This MDP is therefore much easier and maybe
-    useful for quickly testing your algorithms.
-
-### Mission Space
-Depending on the `obstacle_type` parameter:
-- `Lava` - "avoid the lava and get to the green goal square"
-- otherwise - "find the opening and 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/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. The agent falls into lava.
-3. Timeout (see `max_steps`).
-
-### Registered Configurations
-
-S: size of the map SxS.
-N: number of valid crossings across lava or walls from the starting position
-to the goal
-
-- `Lava` :
-    - `MiniGrid-LavaCrossingS9N1-v0`
-    - `MiniGrid-LavaCrossingS9N2-v0`
-    - `MiniGrid-LavaCrossingS9N3-v0`
-    - `MiniGrid-LavaCrossingS11N5-v0`
-
-- otherwise :
-    - `MiniGrid-SimpleCrossingS9N1-v0`
-    - `MiniGrid-SimpleCrossingS9N2-v0`
-    - `MiniGrid-SimpleCrossingS9N3-v0`
-    - `MiniGrid-SimpleCrossingS11N5-v0`

docs/environments/unlock.md

@@ -1,51 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Unlock
----
-
-
-# Unlock
-
-### Description
-
-The agent has to open a locked door. This environment can be solved without
-relying on language.
-
-### Mission Space
-
-"open the door"
-
-### 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       | 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 opens the door.
-2. Timeout (see `max_steps`).
-
-### Registered Configurations
-
-- `MiniGrid-Unlock-v0`

docs/environments/unlock_pickup.md

@@ -1,54 +0,0 @@
----
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
-title: Unlock Pickup
----
-
-
-# Unlock Pickup
-
-### Description
-
-The agent has to pick up a box which is placed in another room, behind a
-locked door. This environment can be solved without relying on language.
-
-### Mission Space
-
-"pick up the {color} box"
-
-{color} is the color of the box. 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 picks up the correct box.
-2. Timeout (see `max_steps`).
-
-### Registered Configurations
-
-- `MiniGrid-Unlock-v0`

docs/scripts/gen_mds.py

@@ -58,7 +58,7 @@ for env_name, env_spec in filtered_envs.items():
     )
 
     front_matter = f"""---
-AUTOGENERATED: DO NOT EDIT FILE DIRECTLY
+autogenerated:
 title: {title_env_name}
 ---
 """