Removed FourRooomQA environment

README.md

@@ -214,25 +214,3 @@ 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.
-
-### Four room question answering environment
-
-Registered configurations:
-- `MiniGrid-FourRoomQA-v0`
-
-<p align="center">
-<img src="/figures/fourroomqa-env.png">
-</p>
-
-This environment is inspired by the
-[Embodied Question Answering](https://arxiv.org/abs/1711.11543) paper. The question are of the form:
-
-> Are there any keys in the red room?
-
-There are four colored rooms, and the agent starts at a random position in the grid.
-Multiple objects of different types and colors are also placed at random
-positions in random rooms. A question and answer pair is generated, the
-question is given to the agent as an observation, and the agent has a limited
-number of time steps to explore the environment and produce a response. This
-environment can be easily modified to add more question types or to diversify
-the way the questions are phrased.

gym_minigrid/envs/__init__.py

@@ -6,6 +6,5 @@ from gym_minigrid.envs.gotoobject import *
 from gym_minigrid.envs.gotodoor import *
 from gym_minigrid.envs.putnear import *
 from gym_minigrid.envs.lockedroom import *
-from gym_minigrid.envs.fourroomqa import *
 from gym_minigrid.envs.playground_v0 import *
 from gym_minigrid.envs.roomgrid import *

gym_minigrid/envs/fourroomqa.py

@@ -1,211 +0,0 @@
-from gym_minigrid.minigrid import *
-from gym_minigrid.register import register
-
-class Room:
-    def __init__(
-        self,
-        top,
-        size,
-        color,
-        objects
-    ):
-        self.top = top
-        self.size = size
-
-        # Color of the room
-        self.color = color
-
-        # List of objects contained
-        self.objects = objects
-
-class FourRoomQAEnv(MiniGridEnv):
-    """
-    Environment to experiment with embodied question answering
-    https://arxiv.org/abs/1711.11543
-    """
-
-    # Enumeration of possible actions
-    class Actions(IntEnum):
-        left = 0
-        right = 1
-        forward = 2
-        toggle = 3
-        wait = 4
-        answer = 5
-
-    def __init__(self, size=16):
-        assert size >= 10
-        super(FourRoomQAEnv, self).__init__(gridSize=size, maxSteps=8*size)
-
-        # Action enumeration for this environment
-        self.actions = FourRoomQAEnv.Actions
-
-        # TODO: dictionary action_space, to include answer sentence?
-        # Actions are discrete integer values
-        self.action_space = spaces.Discrete(len(self.actions))
-
-        self.reward_range = (-1000, 1000)
-
-    def _randPos(self, room, border=1):
-        return (
-            self._randInt(
-                room.top[0] + border,
-                room.top[0] + room.size[0] - border
-            ),
-            self._randInt(
-                room.top[1] + border,
-                room.top[1] + room.size[1] - border
-            ),
-        )
-
-    def _genGrid(self, width, height):
-        self.grid = Grid(width, height)
-
-        # Horizontal and vertical split indices
-        vSplitIdx = self._randInt(5, width-4)
-        hSplitIdx = self._randInt(5, height-4)
-
-        # Create the four rooms
-        self.rooms = []
-        self.rooms.append(Room(
-            (0, 0),
-            (vSplitIdx, hSplitIdx),
-            'red',
-            []
-        ))
-        self.rooms.append(Room(
-            (vSplitIdx, 0),
-            (width - vSplitIdx, hSplitIdx),
-            'purple',
-            []
-        ))
-        self.rooms.append(Room(
-            (0, hSplitIdx),
-            (vSplitIdx, height - hSplitIdx),
-            'blue',
-            []
-        ))
-        self.rooms.append(Room(
-            (vSplitIdx, hSplitIdx),
-            (width - vSplitIdx, height - hSplitIdx),
-            'yellow',
-            []
-        ))
-
-        # Place the room walls
-        for room in self.rooms:
-            x, y = room.top
-            w, h = room.size
-
-            # Horizontal walls
-            for i in range(w):
-                self.grid.set(x + i, y, Wall(room.color))
-                self.grid.set(x + i, y + h - 1, Wall(room.color))
-
-            # Vertical walls
-            for j in range(h):
-                self.grid.set(x, y + j, Wall(room.color))
-                self.grid.set(x + w - 1, y + j, Wall(room.color))
-
-        # Place wall openings connecting the rooms
-        hIdx = self._randInt(1, hSplitIdx-1)
-        self.grid.set(vSplitIdx, hIdx, None)
-        self.grid.set(vSplitIdx-1, hIdx, None)
-        hIdx = self._randInt(hSplitIdx+1, height-1)
-        self.grid.set(vSplitIdx, hIdx, None)
-        self.grid.set(vSplitIdx-1, hIdx, None)
-
-        vIdx = self._randInt(1, vSplitIdx-1)
-        self.grid.set(vIdx, hSplitIdx, None)
-        self.grid.set(vIdx, hSplitIdx-1, None)
-        vIdx = self._randInt(vSplitIdx+1, width-1)
-        self.grid.set(vIdx, hSplitIdx, None)
-        self.grid.set(vIdx, hSplitIdx-1, None)
-
-        # Select a random position for the agent to start at
-        self.startDir = self._randInt(0, 4)
-        room = self._randElem(self.rooms)
-        self.startPos = self._randPos(room)
-
-        # Possible object types and colors
-        types = ['key', 'ball', 'box']
-        colors = list(COLORS.keys())
-
-        # Place a number of random objects
-        numObjs = self._randInt(1, 10)
-        for i in range(0, numObjs):
-            # Generate a random object
-            objType = self._randElem(types)
-            objColor = self._randElem(colors)
-            if objType == 'key':
-                obj = Key(objColor)
-            elif objType == 'ball':
-                obj = Ball(objColor)
-            elif objType == 'box':
-                obj = Box(objColor)
-
-            # Pick a random position that doesn't overlap with anything
-            while True:
-                room = self._randElem(self.rooms)
-                pos = self._randPos(room, border=2)
-                if pos == self.startPos:
-                    continue
-                if self.grid.get(*pos) != None:
-                    continue
-                self.grid.set(*pos, obj)
-                break
-
-            room.objects.append(obj)
-
-        # Question examples:
-        # - What color is the X?
-        # - What color is the X in the ROOM?
-        # - What room is the X located in?
-        # - What color is the X in the blue room?
-        # - How many rooms contain chairs?
-        # - How many keys are there in the yellow room?
-        # - How many <OBJs> in the <ROOM>?
-
-        # Pick a random room to be the subject of the question
-        room = self._randElem(self.rooms)
-
-        # Pick a random object type
-        objType = self._randElem(types)
-
-        # Count the number of objects of this type in the room
-        count = len(list(filter(lambda o: o.type == objType, room.objects)))
-
-        # TODO: identify unique objects
-
-        self.mission = "Are there any %ss in the %s room?" % (objType, room.color)
-        self.answer = "yes" if count > 0 else "no"
-
-        # TODO: how many X in the Y room question type
-
-    def step(self, action):
-        if isinstance(action, dict):
-            answer = action['answer']
-            action = action['action']
-        else:
-            answer = ''
-
-        if action == self.actions.answer:
-            # To the superclass, this action behaves like a noop
-            obs, reward, done, info = MiniGridEnv.step(self, self.actions.wait)
-            done = True
-
-            if answer == self.mission:
-                reward = 1000 - self.stepCount
-            else:
-                reward = -1000
-
-        else:
-            # Let the superclass handle the action
-            obs, reward, done, info = MiniGridEnv.step(self, action)
-
-        return obs, reward, done, info
-
-register(
-    id='MiniGrid-FourRoomQA-v0',
-    entry_point='gym_minigrid.envs:FourRoomQAEnv'
-)