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SocialAISchool / gym-minigrid /gym_minigrid /minigrid.py

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Moving LLM obs to text in textworld utils, bugfixes.

11bd154 about 1 year ago

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	import math
	import random
	import hashlib
	import gym
	from enum import IntEnum
	import numpy as np
	from gym import error, spaces, utils
	from gym.utils import seeding
	from .rendering import *
	from abc import ABC, abstractmethod
	import warnings
	import astar

	import traceback
	import warnings
	from functools import wraps

	SocialAINPCActionsDict = {
	"go_forward": 0,
	"rotate_left": 1,
	"rotate_right": 2,
	"toggle_action": 3,
	"point_stop_point": 4,
	"point_E": 5,
	"point_S": 6,
	"point_W": 7,
	"point_N": 8,
	"stop_point": 9,
	"no_op": 10
	}

	point_dir_encoding = {
	"point_E": 0,
	"point_S": 1,
	"point_W": 2,
	"point_N": 3,
	}

	def get_traceback():
	tb = traceback.extract_stack()
	return "".join(traceback.format_list(tb)[:-1])


	# Size in pixels of a tile in the full-scale human view
	TILE_PIXELS = 32

	# Map of color names to RGB values
	COLORS = {
	'red' : np.array([255, 0, 0]),
	'green' : np.array([0, 255, 0]),
	'blue' : np.array([0, 0, 255]),
	'purple': np.array([112, 39, 195]),
	'yellow': np.array([255, 255, 0]),
	'grey' : np.array([100, 100, 100]),
	'brown': np.array([82, 36, 19])
	}

	COLOR_NAMES = sorted(list(COLORS.keys()))

	# Used to map colors to integers
	COLOR_TO_IDX = {
	'red' : 0,
	'green' : 1,
	'blue' : 2,
	'purple': 3,
	'yellow': 4,
	'grey' : 5,
	'brown' : 6,
	}

	IDX_TO_COLOR = dict(zip(COLOR_TO_IDX.values(), COLOR_TO_IDX.keys()))

	# Map of object type to integers
	OBJECT_TO_IDX = {
	'unseen' : 0,
	'empty' : 1,
	'wall' : 2,
	'floor' : 3,
	'door' : 4,
	'key' : 5,
	'ball' : 6,
	'box' : 7,
	'goal' : 8,
	'lava' : 9,
	'agent' : 10,
	'npc' : 11,
	'switch' : 12,
	'lockablebox' : 13,
	'apple' : 14,
	'applegenerator' : 15,
	'generatorplatform': 16,
	'marble' : 17,
	'marbletee' : 18,
	'fence' : 19,
	'remotedoor' : 20,
	'lever' : 21,
	}

	IDX_TO_OBJECT = dict(zip(OBJECT_TO_IDX.values(), OBJECT_TO_IDX.keys()))

	# Map of state names to integers
	STATE_TO_IDX = {
	'open' : 0,
	'closed': 1,
	'locked': 2,
	}

	# Map of agent direction indices to vectors
	DIR_TO_VEC = [
	# Pointing right (positive X)
	np.array((1, 0)),
	# Down (positive Y)
	np.array((0, 1)),
	# Pointing left (negative X)
	np.array((-1, 0)),
	# Up (negative Y)
	np.array((0, -1)),
	]

	class WorldObj:
	"""
	Base class for grid world objects
	"""

	def __init__(self, type, color):
	assert type in OBJECT_TO_IDX, type
	assert color in COLOR_TO_IDX, color
	self.type = type
	self.color = color
	self.contains = None

	# Initial position of the object
	self.init_pos = None

	# Current position of the object
	self.cur_pos = np.array((0, 0))

	def can_overlap(self):
	"""Can the agent overlap with this?"""
	return False

	def can_push(self):
	"""Can the agent push the object?"""
	return False

	def can_pickup(self):
	"""Can the agent pick this up?"""
	return False

	def can_contain(self):
	"""Can this contain another object?"""
	return False

	def see_behind(self):
	"""Can the agent see behind this object?"""
	return True

	def toggle(self, env, pos):
	"""Method to trigger/toggle an action this object performs"""
	return False

	def encode(self, nb_dims=3, absolute_coordinates=False):
	"""Encode the a description of this object as a nb_dims-tuple of integers"""
	if absolute_coordinates:
	core = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color])
	else:
	core = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color])

	return core + (0,) * (nb_dims - len(core))

	def cache(self, args, *kwargs):
	"""Used for cached rendering."""
	return self.encode(args, *kwargs)

	@staticmethod
	def decode(type_idx, color_idx, state):
	"""Create an object from a 3-tuple state description"""

	obj_type = IDX_TO_OBJECT[type_idx]
	color = IDX_TO_COLOR[color_idx]

	if obj_type == 'empty' or obj_type == 'unseen':
	return None

	if obj_type == 'wall':
	v = Wall(color)
	elif obj_type == 'floor':
	v = Floor(color)
	elif obj_type == 'ball':
	v = Ball(color)
	elif obj_type == 'marble':
	v = Marble(color)
	elif obj_type == 'apple':
	eaten = state == 1
	v = Apple(color, eaten=eaten)
	elif obj_type == 'apple_generator':
	is_pressed = state == 2
	v = AppleGenerator(color, is_pressed=is_pressed)
	elif obj_type == 'key':
	v = Key(color)
	elif obj_type == 'box':
	v = Box(color)
	elif obj_type == 'lockablebox':
	is_locked = state == 2
	v = LockableBox(color, is_locked=is_locked)
	elif obj_type == 'door':
	# State, 0: open, 1: closed, 2: locked
	is_open = state == 0
	is_locked = state == 2
	v = Door(color, is_open, is_locked)
	elif obj_type == 'remotedoor':
	# State, 0: open, 1: closed
	is_open = state == 0
	v = RemoteDoor(color, is_open)
	elif obj_type == 'goal':
	v = Goal()
	elif obj_type == 'lava':
	v = Lava()
	elif obj_type == 'fence':
	v = Fence()
	elif obj_type == 'switch':
	v = Switch(color, is_on=state)
	elif obj_type == 'lever':
	v = Lever(color, is_on=state)
	elif obj_type == 'npc':
	warnings.warn("NPC's internal state cannot be decoded. Only the icon is shown.")
	v = NPC(color)
	v.npc_type=0
	else:
	assert False, "unknown object type in decode '%s'" % obj_type

	return v

	def render(self, r):
	"""Draw this object with the given renderer"""
	raise NotImplementedError


	class BlockableWorldObj(WorldObj):

	def __init__(self, type, color, block_set):
	super(BlockableWorldObj, self).__init__(type, color)
	self.block_set = block_set
	self.blocked = False


	def can_push(self):
	return True

	def push(self, args, *kwargs):
	return self.block_block_set()

	def toggle(self, args, *kwargs):
	return self.block_block_set()

	def block_block_set(self):
	"""A function that blocks the block set"""
	if not self.blocked:
	if self.block_set is not None:
	# cprint("BLOCKED!", "red")
	for e in self.block_set:
	e.block()

	return True

	else:
	return False

	def block(self):
	self.blocked = True


	class Goal(WorldObj):
	def __init__(self):
	super().__init__('goal', 'green')

	def can_overlap(self):
	return True

	def render(self, img):
	fill_coords(img, point_in_rect(0, 1, 0, 1), COLORS[self.color])


	class Floor(WorldObj):
	"""
	Colored floor tile the agent can walk over
	"""

	def __init__(self, color='blue'):
	super().__init__('floor', color)

	def can_overlap(self):
	return True

	def render(self, img):
	# Give the floor a pale color
	color = COLORS[self.color] / 2
	fill_coords(img, point_in_rect(0.031, 1, 0.031, 1), color)


	class Lava(WorldObj):
	def __init__(self):
	super().__init__('lava', 'red')

	def can_overlap(self):
	return True

	def render(self, img):
	c = (255, 128, 0)

	# Background color
	fill_coords(img, point_in_rect(0, 1, 0, 1), c)

	# Little waves
	for i in range(3):
	ylo = 0.3 + 0.2 * i
	yhi = 0.4 + 0.2 * i
	fill_coords(img, point_in_line(0.1, ylo, 0.3, yhi, r=0.03), (0,0,0))
	fill_coords(img, point_in_line(0.3, yhi, 0.5, ylo, r=0.03), (0,0,0))
	fill_coords(img, point_in_line(0.5, ylo, 0.7, yhi, r=0.03), (0,0,0))
	fill_coords(img, point_in_line(0.7, yhi, 0.9, ylo, r=0.03), (0,0,0))


	class Fence(WorldObj):
	"""
	Same as Lava but can't overlap.
	"""
	def __init__(self):
	super().__init__('fence', 'grey')

	def can_overlap(self):
	return False

	def render(self, img):
	c = COLORS[self.color]

	# ugly fence
	fill_coords(img, point_in_rect(
	0.1, 0.9, 0.5, 0.9
	# (0.15, 0.9),
	# (0.10, 0.5),
	# (0.95, 0.9),
	# (0.90, 0.5),
	# (0.10, 0.9),
	# (0.10, 0.5),
	# (0.95, 0.9),
	# (0.95, 0.5),
	), c)
	fill_coords(img, point_in_quadrangle(
	# (0.15, 0.9),
	# (0.10, 0.5),
	# (0.95, 0.9),
	# (0.90, 0.5),
	(0.10, 0.9),
	(0.10, 0.5),
	(0.95, 0.9),
	(0.95, 0.5),
	), c)
	return

	# preety fence
	fill_coords(img, point_in_quadrangle(
	(0.15, 0.3125),
	(0.15, 0.4125),
	(0.85, 0.4875),
	(0.85, 0.5875),
	), c)

	# h2
	fill_coords(img, point_in_quadrangle(
	(0.15, 0.6125),
	(0.15, 0.7125),
	(0.85, 0.7875),
	(0.85, 0.8875),
	), c)

	# vm
	fill_coords(img, point_in_quadrangle(
	(0.45, 0.2875),
	(0.45, 0.8875),
	(0.55, 0.3125),
	(0.55, 0.9125),
	), c)
	fill_coords(img, point_in_triangle(
	(0.45, 0.2875),
	(0.55, 0.3125),
	(0.5, 0.25),
	), c)

	# vl
	fill_coords(img, point_in_quadrangle(
	(0.25, 0.2375),
	(0.25, 0.8375),
	(0.35, 0.2625),
	(0.35, 0.8625),
	), c)
	# vl
	fill_coords(img, point_in_triangle(
	(0.25, 0.2375),
	(0.35, 0.2625),
	(0.3, 0.2),
	), c)


	# vr
	fill_coords(img, point_in_quadrangle(
	(0.65, 0.3375),
	(0.65, 0.9375),
	(0.75, 0.3625),
	(0.75, 0.9625),
	), c)
	fill_coords(img, point_in_triangle(
	(0.65, 0.3375),
	(0.75, 0.3625),
	(0.7, 0.3),
	), c)


	class Wall(WorldObj):
	def __init__(self, color='grey'):
	super().__init__('wall', color)

	def see_behind(self):
	return False

	def render(self, img):
	fill_coords(img, point_in_rect(0, 1, 0, 1), COLORS[self.color])


	class Lever(BlockableWorldObj):
	def __init__(self, color, object=None, is_on=False, block_set=None, active_steps=None):
	super().__init__('lever', color, block_set)
	self.is_on = is_on
	self.object = object

	self.active_steps = active_steps
	self.countdown = None # countdown timer

	self.was_activated = False

	if self.block_set is not None:
	if self.is_on:
	raise ValueError("If using a block set, a Switch must be initialized as OFF")

	def can_overlap(self):
	"""The agent can only walk over this cell when the door is open"""
	return False

	def see_behind(self):
	return True

	def step(self):
	if self.countdown is not None:
	self.countdown = self.countdown - 1

	if self.countdown <= 0:
	# if nothing is on the door, close the door and deactivate timer
	self.toggle()
	self.countdown = None

	def toggle(self, env=None, pos=None):

	if self.blocked:
	return False

	if self.was_activated and not self.is_on:
	# cannot be activated twice
	return False

	self.is_on = not self.is_on

	if self.is_on:
	if self.active_steps is not None:
	# activate countdown to shutdown
	self.countdown = self.active_steps
	self.was_activated = True

	if self.object is not None:
	# open object
	self.object.open_close()

	if self.is_on:
	self.block_block_set()

	return True

	def block(self):
	self.blocked = True

	def encode(self, nb_dims=3, absolute_coordinates=False):
	"""Encode the a description of this object as a 3-tuple of integers"""

	# State, 0: off, 1: on
	state = 1 if self.is_on else 0

	count = self.countdown if self.countdown is not None else 255

	if absolute_coordinates:
	v = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color], state, count)
	else:
	v = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], state, count)

	v += (0,) * (nb_dims-len(v))

	return v

	def render(self, img):
	c = COLORS[self.color]
	black = (0, 0, 0)

	# off_angle = -math.pi/3
	off_angle = -math.pi/2
	on_angle = -math.pi/8


	rotating_lever_shapes = []
	rotating_lever_shapes.append((point_in_rect(0.5, 0.9, 0.77, 0.83), c))
	rotating_lever_shapes.append((point_in_circle(0.9, 0.8, 0.1), c))

	rotating_lever_shapes.append((point_in_circle(0.5, 0.8, 0.08), c))

	if self.is_on:
	if self.countdown is None:
	angle = on_angle
	else:
	angle = (self.countdown/self.active_steps) * (on_angle-off_angle) + off_angle

	else:
	angle = off_angle

	fill_coords(img, point_in_circle_clip(0.5, 0.8, 0.12, theta_end=-math.pi), c)
	# fill_coords(img, point_in_circle_clip(0.5, 0.8, 0.08, theta_end=-math.pi), black)

	rotating_lever_shapes = [(rotate_fn(v, cx=0.5, cy=0.8, theta=angle), col) for v, col in rotating_lever_shapes]

	for v, col in rotating_lever_shapes:
	fill_coords(img, v, col)

	fill_coords(img, point_in_rect(0.2, 0.8, 0.78, 0.82), c)
	fill_coords(img, point_in_circle(0.5, 0.8, 0.03), (0, 0, 0))


	class RemoteDoor(BlockableWorldObj):
	"""Door that are unlocked by a lever"""
	def __init__(self, color, is_open=False, block_set=None):
	super().__init__('remotedoor', color, block_set)
	self.is_open = is_open

	def can_overlap(self):
	"""The agent can only walk over this cell when the door is open"""
	return self.is_open

	def see_behind(self):
	return self.is_open

	# def toggle(self, env, pos=None):
	# return False

	def open_close(self):
	# If the player has the right key to open the door

	self.is_open = not self.is_open
	return True

	def encode(self, nb_dims=3, absolute_coordinates=False):
	"""Encode the a description of this object as a 3-tuple of integers"""

	# State, 0: open, 1: closed
	state = 0 if self.is_open else 1

	if absolute_coordinates:
	v = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color], state)
	else:
	v = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], state)

	v += (0,) * (nb_dims-len(v))
	return v

	def block(self):
	self.blocked = True

	def render(self, img):
	c = COLORS[self.color]

	if self.is_open:
	fill_coords(img, point_in_rect(0.88, 1.00, 0.00, 1.00), c)
	fill_coords(img, point_in_rect(0.92, 0.96, 0.04, 0.96), (0,0,0))
	else:

	fill_coords(img, point_in_rect(0.00, 1.00, 0.00, 1.00), c)
	fill_coords(img, point_in_rect(0.04, 0.96, 0.04, 0.96), (0,0,0))
	fill_coords(img, point_in_rect(0.08, 0.92, 0.08, 0.92), c)
	fill_coords(img, point_in_rect(0.12, 0.88, 0.12, 0.88), (0,0,0))

	# wifi symbol
	fill_coords(img, point_in_circle_clip(cx=0.5, cy=0.8, r=0.5, theta_start=-np.pi/3, theta_end=-2*np.pi/3), c)
	fill_coords(img, point_in_circle_clip(cx=0.5, cy=0.8, r=0.45, theta_start=-np.pi/3, theta_end=-2*np.pi/3), (0,0,0))
	fill_coords(img, point_in_circle_clip(cx=0.5, cy=0.8, r=0.4, theta_start=-np.pi/3, theta_end=-2*np.pi/3), c)
	fill_coords(img, point_in_circle_clip(cx=0.5, cy=0.8, r=0.35, theta_start=-np.pi/3, theta_end=-2*np.pi/3), (0,0,0))
	fill_coords(img, point_in_circle_clip(cx=0.5, cy=0.8, r=0.3, theta_start=-np.pi/3, theta_end=-2*np.pi/3), c)
	fill_coords(img, point_in_circle_clip(cx=0.5, cy=0.8, r=0.25, theta_start=-np.pi/3, theta_end=-2*np.pi/3), (0,0,0))
	fill_coords(img, point_in_circle_clip(cx=0.5, cy=0.8, r=0.2, theta_start=-np.pi/3, theta_end=-2*np.pi/3), c)
	fill_coords(img, point_in_circle_clip(cx=0.5, cy=0.8, r=0.15, theta_start=-np.pi/3, theta_end=-2*np.pi/3), (0,0,0))
	fill_coords(img, point_in_circle_clip(cx=0.5, cy=0.8, r=0.1, theta_start=-np.pi/3, theta_end=-2*np.pi/3), c)

	return


	class Door(BlockableWorldObj):
	def __init__(self, color, is_open=False, is_locked=False, block_set=None):
	super().__init__('door', color, block_set)
	self.is_open = is_open
	self.is_locked = is_locked

	def can_overlap(self):
	"""The agent can only walk over this cell when the door is open"""
	return self.is_open

	def see_behind(self):
	return self.is_open

	def toggle(self, env, pos=None):

	if self.blocked:
	return False

	# If the player has the right key to open the door
	if self.is_locked:
	if isinstance(env.carrying, Key) and env.carrying.color == self.color:
	self.is_locked = False
	self.is_open = True
	ret = True
	ret = False

	else:
	self.is_open = not self.is_open
	ret = True

	self.block_block_set()

	return ret


	def encode(self, nb_dims=3, absolute_coordinates=False):
	"""Encode the a description of this object as a 3-tuple of integers"""

	# State, 0: open, 1: closed, 2: locked
	if self.is_open:
	state = 0
	elif self.is_locked:
	state = 2
	elif not self.is_open:
	state = 1

	if absolute_coordinates:
	v = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color], state)
	else:
	v = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], state)

	v += (0,) * (nb_dims-len(v))
	return v

	def render(self, img):
	c = COLORS[self.color]

	if self.is_open:
	fill_coords(img, point_in_rect(0.88, 1.00, 0.00, 1.00), c)
	fill_coords(img, point_in_rect(0.92, 0.96, 0.04, 0.96), (0,0,0))
	return

	# Door frame and door
	if self.is_locked:
	fill_coords(img, point_in_rect(0.00, 1.00, 0.00, 1.00), c)
	fill_coords(img, point_in_rect(0.06, 0.94, 0.06, 0.94), 0.45 * np.array(c))

	# Draw key slot
	fill_coords(img, point_in_rect(0.52, 0.75, 0.50, 0.56), c)
	else:
	fill_coords(img, point_in_rect(0.00, 1.00, 0.00, 1.00), c)
	fill_coords(img, point_in_rect(0.04, 0.96, 0.04, 0.96), (0,0,0))
	fill_coords(img, point_in_rect(0.08, 0.92, 0.08, 0.92), c)
	fill_coords(img, point_in_rect(0.12, 0.88, 0.12, 0.88), (0,0,0))

	# Draw door handle
	fill_coords(img, point_in_circle(cx=0.75, cy=0.50, r=0.08), c)


	class Switch(BlockableWorldObj):
	def __init__(self, color, lockable_object=None, is_on=False, no_turn_off=True, no_light=True, locker_switch=False, block_set=None):
	super().__init__('switch', color, block_set)
	self.is_on = is_on
	self.lockable_object = lockable_object
	self.no_turn_off = no_turn_off
	self.no_light = no_light
	self.locker_switch = locker_switch

	if self.block_set is not None:

	if self.is_on:
	raise ValueError("If using a block set, a Switch must be initialized as OFF")

	if not self.no_turn_off:
	raise ValueError("If using a block set, a Switch must be initialized can't be turned off")


	def can_overlap(self):
	"""The agent can only walk over this cell when the door is open"""
	return False

	def see_behind(self):
	return True

	def toggle(self, env, pos=None):

	if self.blocked:
	return False

	if self.is_on:
	if self.no_turn_off:
	return False

	self.is_on = not self.is_on
	if self.lockable_object is not None:
	if self.locker_switch:
	# locker/unlocker switch
	self.lockable_object.is_locked = not self.lockable_object.is_locked
	else:
	# opener switch
	self.lockable_object.toggle(env, pos)


	if self.is_on:
	self.block_block_set()

	if self.no_turn_off:
	# assert that obj is toggled only once
	assert not hasattr(self, "was_toggled")
	self.was_toggled = True

	return True

	def block(self):
	self.blocked = True

	def encode(self, nb_dims=3, absolute_coordinates=False):
	"""Encode the a description of this object as a 3-tuple of integers"""

	# State, 0: off, 1: on
	state = 1 if self.is_on else 0

	if self.no_light:
	state = 0

	if absolute_coordinates:
	v = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color], state)
	else:
	v = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], state)

	v += (0,) * (nb_dims-len(v))

	return v


	def render(self, img):
	c = COLORS[self.color]

	# Door frame and door
	if self.is_on and not self.no_light:
	fill_coords(img, point_in_rect(0.00, 1.00, 0.00, 1.00), c)
	fill_coords(img, point_in_rect(0.04, 0.96, 0.04, 0.96), (0,0,0))
	fill_coords(img, point_in_rect(0.08, 0.92, 0.08, 0.92), c)
	fill_coords(img, point_in_rect(0.12, 0.88, 0.12, 0.88), 0.45 * np.array(c))

	else:

	fill_coords(img, point_in_rect(0.00, 1.00, 0.00, 1.00), c)
	fill_coords(img, point_in_rect(0.04, 0.96, 0.04, 0.96), (0,0,0))
	fill_coords(img, point_in_rect(0.08, 0.92, 0.08, 0.92), c)
	fill_coords(img, point_in_rect(0.12, 0.88, 0.12, 0.88), (0,0,0))


	class Key(WorldObj):
	def __init__(self, color='blue'):
	super(Key, self).__init__('key', color)

	def can_pickup(self):
	return True

	def render(self, img):
	c = COLORS[self.color]

	# Vertical quad
	fill_coords(img, point_in_rect(0.50, 0.63, 0.31, 0.88), c)

	# Teeth
	fill_coords(img, point_in_rect(0.38, 0.50, 0.59, 0.66), c)
	fill_coords(img, point_in_rect(0.38, 0.50, 0.81, 0.88), c)

	# Ring
	fill_coords(img, point_in_circle(cx=0.56, cy=0.28, r=0.190), c)
	fill_coords(img, point_in_circle(cx=0.56, cy=0.28, r=0.064), (0,0,0))


	class MarbleTee(WorldObj):
	def __init__(self, color="red"):
	super(MarbleTee, self).__init__('marbletee', color)

	def can_pickup(self):
	return False

	def can_push(self):
	return False

	def render(self, img):
	c = COLORS[self.color]

	fill_coords(img, point_in_quadrangle(
	(0.2, 0.5),
	(0.8, 0.5),
	(0.4, 0.6),
	(0.6, 0.6),
	), c)

	fill_coords(img, point_in_triangle(
	(0.4, 0.6),
	(0.6, 0.6),
	(0.5, 0.9),
	), c)


	class Marble(WorldObj):
	def __init__(self, color='blue', env=None):
	super(Marble, self).__init__('marble', color)
	self.is_tagged = False
	self.moving_dir = None
	self.env = env
	self.was_pushed = False
	self.tee = MarbleTee(color)
	self.tee_uncovered = False

	def can_pickup(self):
	return True

	def step(self):
	if self.moving_dir is not None:
	prev_pos = self.cur_pos
	self.go_forward()

	if any(prev_pos != self.cur_pos) and not self.tee_uncovered:
	assert self.was_pushed

	# if Marble was moved for the first time, uncover the Tee
	# self.env.grid.set(*prev_pos, self.tee)
	self.env.put_obj_np(self.tee, prev_pos)
	self.tee_uncovered = True

	@property
	def is_moving(self):
	return self.moving_dir is not None

	@property
	def dir_vec(self):
	"""
	Get the direction vector for the agent, pointing in the direction
	of forward movement.
	"""
	if self.moving_dir is not None:
	return DIR_TO_VEC[self.moving_dir]
	else:
	return np.array((0, 0))

	@property
	def front_pos(self):
	"""
	Get the position of the cell that is right in front of the agent
	"""
	return self.cur_pos + self.dir_vec

	def go_forward(self):
	# Get the position in front of the agent
	fwd_pos = self.front_pos

	# Get the contents of the cell in front of the agent
	fwd_cell = self.env.grid.get(*fwd_pos)
	# Don't move if you are going to collide
	if fwd_pos.tolist() != self.env.agent_pos.tolist() and (fwd_cell is None or fwd_cell.can_overlap()):
	self.env.grid.set(*self.cur_pos, None)
	self.env.grid.set(*fwd_pos, self)
	self.cur_pos = fwd_pos
	return True

	# push object if pushable
	if fwd_pos.tolist() != self.env.agent_pos.tolist() and (fwd_cell is not None and fwd_cell.can_push()):
	fwd_cell.push(push_dir=self.moving_dir, pusher=self)
	self.moving_dir = None
	return True

	else:
	self.moving_dir = None
	return False

	def can_push(self):
	return True

	def push(self, push_dir, pusher=None):
	if type(push_dir) is not int:
	raise ValueError("Direction must be of type int and is of type {}".format(type(push_dir)))

	self.moving_dir = push_dir
	if self.moving_dir is not None:
	self.was_pushed = True

	def render(self, img):
	color = COLORS[self.color]
	if self.is_tagged:
	color = color / 2

	fill_coords(img, point_in_circle(0.5, 0.5, 0.20), color)
	fill_coords(img, point_in_circle(0.55, 0.45, 0.07), (0, 0, 0))

	def tag(self,):
	self.is_tagged = True

	def encode(self, nb_dims=3, absolute_coordinates=False):
	"""Encode the a description of this object as a nb_dims-tuple of integers"""
	if absolute_coordinates:
	core = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color])
	else:
	core = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color])

	return core + (1 if self.is_tagged else 0,) * (nb_dims - len(core))


	class Ball(WorldObj):
	def __init__(self, color='blue'):
	super(Ball, self).__init__('ball', color)
	self.is_tagged = False

	def can_pickup(self):
	return True

	def render(self, img):
	color = COLORS[self.color]
	if self.is_tagged:
	color = color / 2
	fill_coords(img, point_in_circle(0.5, 0.5, 0.31), color)

	def tag(self,):
	self.is_tagged = True

	def encode(self, nb_dims=3, absolute_coordinates=False):
	"""Encode the a description of this object as a nb_dims-tuple of integers"""
	if absolute_coordinates:
	core = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color])
	else:
	core = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color])

	return core + (1 if self.is_tagged else 0,) * (nb_dims - len(core))


	class Apple(WorldObj):
	def __init__(self, color='red', eaten=False):
	super(Apple, self).__init__('apple', color)
	self.is_tagged = False
	self.eaten = eaten
	assert self.color != "yellow"

	def revert(self, color='red', eaten=False):
	self.color = color
	self.is_tagged = False
	self.eaten = eaten
	assert self.color != "yellow"

	def can_pickup(self):
	return False

	def render(self, img):
	color = COLORS[self.color]

	if self.is_tagged:
	color = color / 2

	fill_coords(img, point_in_circle(0.5, 0.5, 0.31), color)
	fill_coords(img, point_in_rect(0.1, 0.9, 0.1, 0.55), (0, 0, 0))
	fill_coords(img, point_in_circle(0.35, 0.5, 0.15), color)
	fill_coords(img, point_in_circle(0.65, 0.5, 0.15), color)

	fill_coords(img, point_in_rect(0.48, 0.52, 0.2, 0.45), COLORS["brown"])

	# quadrangle
	fill_coords(img, point_in_quadrangle(
	(0.52, 0.25),
	(0.65, 0.1),
	(0.75, 0.3),
	(0.90, 0.15),
	), COLORS["green"])


	if self.eaten:
	assert self.color == "yellow"
	fill_coords(img, point_in_circle(0.74, 0.6, 0.23), (0,0,0))
	fill_coords(img, point_in_circle(0.26, 0.6, 0.23), (0,0,0))

	def toggle(self, env, pos):
	if not self.eaten:
	self.eaten = True

	assert self.color != "yellow"
	self.color = "yellow"

	return True

	else:
	assert self.color == "yellow"
	return False

	def tag(self,):
	self.is_tagged = True

	def encode(self, nb_dims=3, absolute_coordinates=False):
	"""Encode the a description of this object as a nb_dims-tuple of integers"""

	# eaten <=> yellow
	assert self.eaten == (self.color == "yellow")
	if absolute_coordinates:
	core = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color])
	else:
	core = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color])

	return core + (1 if self.is_tagged else 0,) * (nb_dims - len(core))


	class GeneratorPlatform(WorldObj):
	def __init__(self, color="red"):
	super(GeneratorPlatform, self).__init__('generatorplatform', color)

	def can_pickup(self):
	return False

	def can_push(self):
	return False

	def render(self, img):
	c = COLORS[self.color]

	fill_coords(img, point_in_circle(0.5, 0.5, 0.2), c)
	fill_coords(img, point_in_circle(0.5, 0.5, 0.18), (0,0,0))

	fill_coords(img, point_in_circle(0.5, 0.5, 0.16), c)
	fill_coords(img, point_in_circle(0.5, 0.5, 0.14), (0,0,0))


	class AppleGenerator(BlockableWorldObj):
	def __init__(self, color="red", is_pressed=False, block_set=None, on_push=None, marble_activation=False):
	super(AppleGenerator, self).__init__('applegenerator', color, block_set)
	self.is_pressed = is_pressed
	self.on_push = on_push
	self.marble_activation = marble_activation

	def can_pickup(self):
	return False

	def block(self):
	self.blocked = True

	def can_push(self):
	return True

	def push(self, push_dir=None, pusher=None):

	if self.marble_activation:
	# check that it is marble that pushed the generator
	if type(pusher) != Marble:
	return self.block_block_set()

	if not self.blocked:
	self.is_pressed = True

	if self.on_push:
	self.on_push()

	return self.block_block_set()

	else:
	return False

	def render(self, img):
	c = COLORS[self.color]

	if not self.marble_activation:
	# Outline
	fill_coords(img, point_in_rect(0.15, 0.85, 0.15, 0.85), c)
	# fill_coords(img, point_in_rect(0.17, 0.83, 0.17, 0.83), (0, 0, 0))
	fill_coords(img, point_in_rect(0.16, 0.84, 0.16, 0.84), (0, 0, 0))

	# Outline
	fill_coords(img, point_in_rect(0.22, 0.78, 0.22, 0.78), c)
	fill_coords(img, point_in_rect(0.24, 0.76, 0.24, 0.76), (0, 0, 0))
	else:
	# Outline
	fill_coords(img, point_in_circle(0.5, 0.5, 0.37), c)
	fill_coords(img, point_in_circle(0.5, 0.5, 0.35), (0, 0, 0))

	# Outline
	fill_coords(img, point_in_circle(0.5, 0.5, 0.32), c)
	fill_coords(img, point_in_circle(0.5, 0.5, 0.30), (0, 0, 0))

	# Apple inside
	fill_coords(img, point_in_circle(0.5, 0.5, 0.2), COLORS["red"])
	# fill_coords(img, point_in_rect(0.18, 0.82, 0.18, 0.55), (0, 0, 0))
	fill_coords(img, point_in_rect(0.30, 0.65, 0.30, 0.55), (0, 0, 0))
	fill_coords(img, point_in_circle(0.42, 0.5, 0.12), COLORS["red"])
	fill_coords(img, point_in_circle(0.58, 0.5, 0.12), COLORS["red"])

	# peteljka
	fill_coords(img, point_in_rect(0.49, 0.50, 0.25, 0.45), COLORS["brown"])

	# leaf
	fill_coords(img, point_in_quadrangle(
	(0.52, 0.32),
	(0.60, 0.21),
	(0.70, 0.34),
	(0.80, 0.23),
	), COLORS["green"])

	def encode(self, nb_dims=3, absolute_coordinates=False):
	"""Encode the a description of this object as a 3-tuple of integers"""

	type = 2 if self.marble_activation else 1

	if absolute_coordinates:
	v = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color], type)
	else:
	v = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], type)

	v += (0,) * (nb_dims - len(v))

	return v


	class Box(WorldObj):
	def __init__(self, color="red", contains=None):
	super(Box, self).__init__('box', color)
	self.contains = contains

	def can_pickup(self):
	return True

	def render(self, img):
	c = COLORS[self.color]

	# Outline
	fill_coords(img, point_in_rect(0.12, 0.88, 0.12, 0.88), c)
	fill_coords(img, point_in_rect(0.18, 0.82, 0.18, 0.82), (0,0,0))

	# Horizontal slit
	fill_coords(img, point_in_rect(0.16, 0.84, 0.47, 0.53), c)

	def toggle(self, env, pos):
	# Replace the box by its contents
	env.grid.set(*pos, self.contains)
	return True


	class LockableBox(BlockableWorldObj):
	def __init__(self, color="red", is_locked=False, contains=None, block_set=None):
	super(LockableBox, self).__init__('lockablebox', color, block_set)
	self.contains = contains
	self.is_locked = is_locked

	self.is_open = False

	def can_pickup(self):
	return True

	def encode(self, nb_dims=3, absolute_coordinates=False):
	"""Encode the a description of this object as a 3-tuple of integers"""

	# State, 0: open, 1: closed, 2: locked
	# 2 and 1 to be consistent with doors
	if self.is_locked:
	state = 2
	else:
	state = 1

	if absolute_coordinates:
	v = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color], state)
	else:
	v = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], state)

	v += (0,) * (nb_dims - len(v))

	return v

	def render(self, img):
	c = COLORS[self.color]

	# Outline
	fill_coords(img, point_in_rect(0.12, 0.88, 0.12, 0.88), c)

	if self.is_locked:
	fill_coords(img, point_in_rect(0.18, 0.82, 0.18, 0.82), 0.45 * np.array(c))
	else:
	fill_coords(img, point_in_rect(0.18, 0.82, 0.18, 0.82), (0, 0, 0))

	# Horizontal slit
	fill_coords(img, point_in_rect(0.16, 0.84, 0.47, 0.53), c)

	def toggle(self, env, pos):
	if self.blocked:
	return False

	if self.is_locked:
	if isinstance(env.carrying, Key) and env.carrying.color == self.color:
	self.is_locked = False
	self.is_open = True
	return True
	return False

	self.is_open = True
	# Replace the box by its contents
	env.grid.set(*pos, self.contains)

	self.block_block_set()

	# assert that obj is toggled only once
	assert not hasattr(self, "was_toggled")
	self.was_toggled = True

	return True

	def block(self):
	self.blocked = True


	class NPC(ABC, WorldObj):
	def __init__(self, color, view_size=7):
	super().__init__('npc', color)
	self.point_dir = 255 # initially no point
	self.introduction_statement = "Help please "
	self.list_of_possible_utterances = NPC.get_list_of_possible_utterances()
	self.view_size = view_size
	self.carrying = False
	self.prim_actions_dict = SocialAINPCActionsDict

	self.reset_last_action()

	@staticmethod
	def get_list_of_possible_utterances():
	return ["no_op"]

	def _npc_action(func):
	"""
	Decorator that logs the last action
	"""
	@wraps(func)
	def func_wrapper(self, args, *kwargs):

	if self.env.add_npc_last_prim_action:
	self.last_action = func.__name__

	return func(self, args, *kwargs)

	return func_wrapper

	def reset_last_action(self):
	self.last_action = "no_op"

	def step(self):
	self.reset_last_action()

	if self.env.hidden_npc:
	info = {
	"prim_action": "no_op",
	"utterance": "no_op",
	"was_introduced_to": self.was_introduced_to
	}
	return None, info

	else:
	return None, None

	def handle_introduction(self, utterance):
	reply, action = None, None
	# introduction and language
	if self.env.parameters.get("Pragmatic_frame_complexity", "No") == "No":

	# only once
	if not self.was_introduced_to:
	self.was_introduced_to = True

	elif self.env.parameters["Pragmatic_frame_complexity"] == "Eye_contact":

	# only first time at eye contact
	if self.is_eye_contact() and not self.was_introduced_to:
	self.was_introduced_to = True

	# if not self.was_introduced_to:
	# rotate to see the agent
	# action = self.look_at_action(self.env.agent_pos)

	elif self.env.parameters["Pragmatic_frame_complexity"] == "Ask":

	# every time asked
	if utterance == self.introduction_statement:
	self.was_introduced_to = True

	elif self.env.parameters["Pragmatic_frame_complexity"] == "Ask_Eye_contact":

	# only first time at eye contact with the introduction statement
	if (self.is_eye_contact() and utterance == self.introduction_statement) and not self.was_introduced_to:
	self.was_introduced_to = True

	# if not self.was_introduced_to:
	# # rotate to see the agent
	# action = self.look_at_action(self.env.agent_pos)

	else:
	raise NotImplementedError()

	return reply, action

	def look_at_action(self, target_pos):
	# rotate to see the target_pos
	wanted_dir = self.compute_wanted_dir(target_pos)
	action = self.compute_turn_action(wanted_dir)
	return action

	@_npc_action
	def rotate_left(self):
	self.npc_dir -= 1
	if self.npc_dir < 0:
	self.npc_dir += 4
	return True

	@_npc_action
	def rotate_right(self):
	self.npc_dir = (self.npc_dir + 1) % 4
	return True

	def path_to_toggle_pos(self, goal_pos):
	"""
	Return the next action from the path to toggling an object at goal_pos
	"""
	if type(goal_pos) != np.ndarray or goal_pos.shape != (2,):
	raise ValueError(f"goal_pos must be a np.ndarray of shape (2,) and is {goal_pos}")

	assert type(self.front_pos) == np.ndarray and self.front_pos.shape == (2,)

	if all(self.front_pos == goal_pos):
	# in front of door
	return self.toggle_action

	else:
	return self.path_to_pos(goal_pos)

	def turn_to_see_agent(self):
	wanted_dir = self.compute_wanted_dir(self.env.agent_pos)
	action = self.compute_turn_action(wanted_dir)
	return action

	def relative_coords(self, x, y):
	"""
	Check if a grid position belongs to the npc's field of view, and returns the corresponding coordinates
	"""

	vx, vy = self.get_view_coords(x, y)

	if vx < 0 or vy < 0 or vx >= self.view_size or vy >= self.view_size:
	return None

	return vx, vy


	def get_view_coords(self, i, j):
	"""
	Translate and rotate absolute grid coordinates (i, j) into the
	npc's partially observable view (sub-grid). Note that the resulting
	coordinates may be negative or outside of the npc's view size.
	"""

	ax, ay = self.cur_pos
	dx, dy = self.dir_vec
	rx, ry = self.right_vec

	# Compute the absolute coordinates of the top-left view corner
	sz = self.view_size
	hs = self.view_size // 2
	tx = ax + (dx * (sz-1)) - (rx * hs)
	ty = ay + (dy * (sz-1)) - (ry * hs)

	lx = i - tx
	ly = j - ty

	# Project the coordinates of the object relative to the top-left
	# corner onto the agent's own coordinate system
	vx = (rxlx + ryly)
	vy = -(dxlx + dyly)

	return vx, vy

	def is_pointing(self):
	return self.point_dir != 255

	def path_to_pos(self, goal_pos):
	"""
	Return the next action from the path to goal_pos
	"""

	if type(goal_pos) != np.ndarray or goal_pos.shape != (2,):
	raise ValueError(f"goal_pos must be a np.ndarray of shape (2,) and is {goal_pos}")

	def neighbors(n):

	n_nd = np.array(n)

	adjacent_positions = [
	n_nd + np.array([ 0, 1]),
	n_nd + np.array([ 0,-1]),
	n_nd + np.array([ 1, 0]),
	n_nd + np.array([-1, 0]),
	]
	adjacent_cells = map(lambda pos: self.env.grid.get(*pos), adjacent_positions)

	# keep the positions that don't have anything on or can_overlap
	neighbors = [
	tuple(pos) for pos, cell in
	zip(adjacent_positions, adjacent_cells) if (
	all(pos == goal_pos)
	or cell is None
	or cell.can_overlap()
	) and not all(pos == self.env.agent_pos)
	]

	for n1 in neighbors:
	yield n1

	def distance(n1, n2):
	return 1

	def cost(n, goal):
	# manhattan
	return int(np.abs(np.array(n) - np.array(goal)).sum())

	# def is_goal_reached(n, goal):
	# return all(n == goal)

	path = astar.find_path(
	# tuples because np.ndarray is not hashable
	tuple(self.cur_pos),
	tuple(goal_pos),
	neighbors_fnct=neighbors,
	heuristic_cost_estimate_fnct=cost,
	distance_between_fnct=distance,
	# is_goal_reached_fnct=is_goal_reached
	)

	if path is None:
	# no possible path
	return None

	path = list(path)
	assert all(path[0] == self.cur_pos)
	next_step = path[1]
	wanted_dir = self.compute_wanted_dir(next_step)

	if self.npc_dir == wanted_dir:
	return self.go_forward

	else:
	return self.compute_turn_action(wanted_dir)

	def gen_obs_grid(self):
	"""
	Generate the sub-grid observed by the npc.
	This method also outputs a visibility mask telling us which grid
	cells the npc can actually see.
	"""
	view_size = self.view_size

	topX, topY, botX, botY = self.env.get_view_exts(dir=self.npc_dir, view_size=view_size, pos=self.cur_pos)

	grid = self.env.grid.slice(topX, topY, view_size, view_size)

	for i in range(self.npc_dir + 1):
	grid = grid.rotate_left()

	# Process ocluders and visibility
	# Note that this incurs some performance cost
	if not self.env.see_through_walls:
	vis_mask = grid.process_vis(agent_pos=(view_size // 2, view_size - 1))
	else:
	vis_mask = np.ones(shape=(grid.width, grid.height), dtype=np.bool)

	# Make it so the npc sees what it's carrying
	# We do this by placing the carried object at the agent's position
	# in the agent's partially observable view
	npc_pos = grid.width // 2, grid.height - 1
	if self.carrying:
	grid.set(*npc_pos, self.carrying)
	else:
	grid.set(*npc_pos, None)

	return grid, vis_mask

	def is_near_agent(self):
	ax, ay = self.env.agent_pos
	wx, wy = self.cur_pos
	if (ax == wx and abs(ay - wy) == 1) or (ay == wy and abs(ax - wx) == 1):
	return True
	return False

	def is_eye_contact(self):
	"""
	Returns true if the agent and the NPC are looking at each other
	"""
	if self.cur_pos[1] == self.env.agent_pos[1]:
	# same y
	if self.cur_pos[0] > self.env.agent_pos[0]:
	return self.npc_dir == 2 and self.env.agent_dir == 0
	else:
	return self.npc_dir == 0 and self.env.agent_dir == 2

	if self.cur_pos[0] == self.env.agent_pos[0]:
	# same x
	if self.cur_pos[1] > self.env.agent_pos[1]:
	return self.npc_dir == 3 and self.env.agent_dir == 1
	else:
	return self.npc_dir == 1 and self.env.agent_dir == 3

	return False

	def compute_wanted_dir(self, target_pos):
	"""
	Computes the direction in which the NPC should look to see target pos
	"""

	distance_vec = target_pos - self.cur_pos
	angle = np.degrees(np.arctan2(*distance_vec))
	if angle < 0:
	angle += 360

	if angle < 45:
	wanted_dir = 1 # S
	elif angle < 135:
	wanted_dir = 0 # E
	elif angle < 225:
	wanted_dir = 3 # N
	elif angle < 315:
	wanted_dir = 2 # W
	elif angle < 360:
	wanted_dir = 1 # S

	return wanted_dir

	def compute_wanted_point_dir(self, target_pos):
	point_dir = self.compute_wanted_dir(target_pos)

	return point_dir

	# dir = 0 # E
	# dir = 1 # S
	# dir = 2 # W
	# dir = 3 # N
	# dir = 255 # no point

	@_npc_action
	def stop_point(self):
	self.point_dir = 255
	return True

	@_npc_action
	def point_E(self):
	self.point_dir = point_dir_encoding["point_E"]
	return True

	@_npc_action
	def point_S(self):
	self.point_dir = point_dir_encoding["point_S"]
	return True

	@_npc_action
	def point_W(self):
	self.point_dir = point_dir_encoding["point_W"]
	return True

	@_npc_action
	def point_N(self):
	self.point_dir = point_dir_encoding["point_N"]
	return True

	def compute_wanted_point_action(self, target_pos):
	point_dir = self.compute_wanted_dir(target_pos)

	if point_dir == point_dir_encoding["point_E"]:
	return self.point_E
	elif point_dir == point_dir_encoding["point_S"]:
	return self.point_S
	elif point_dir == point_dir_encoding["point_W"]:
	return self.point_W
	elif point_dir == point_dir_encoding["point_N"]:
	return self.point_N
	else:
	raise ValueError("Unknown direction {}".format(point_dir))


	def compute_turn_action(self, wanted_dir):
	"""
	Return the action turning for in the direction of wanted_dir
	"""
	if self.npc_dir == wanted_dir:
	# return lambda *args: None
	return None
	if (wanted_dir - self.npc_dir) == 1 or (wanted_dir == 0 and self.npc_dir == 3):
	return self.rotate_right
	if (wanted_dir - self.npc_dir) == - 1 or (wanted_dir == 3 and self.npc_dir == 0):
	return self.rotate_left
	else:
	return self.env._rand_elem([self.rotate_left, self.rotate_right])

	@_npc_action
	def go_forward(self):
	# Get the position in front of the agent
	fwd_pos = self.front_pos

	# Get the contents of the cell in front of the agent
	fwd_cell = self.env.grid.get(*fwd_pos)
	# Don't move if you are going to collide
	if fwd_pos.tolist() != self.env.agent_pos.tolist() and (fwd_cell is None or fwd_cell.can_overlap()):
	self.env.grid.set(*self.cur_pos, None)
	self.env.grid.set(*fwd_pos, self)
	self.cur_pos = fwd_pos
	return True

	# push object if pushable
	if fwd_pos.tolist() != self.env.agent_pos.tolist() and (fwd_cell is not None and fwd_cell.can_push()):
	fwd_cell.push(push_dir=self.npc_dir, pusher=self)

	else:
	return False

	@_npc_action
	def toggle_action(self):
	fwd_pos = self.front_pos
	fwd_cell = self.env.grid.get(*fwd_pos)
	if fwd_cell:
	return fwd_cell.toggle(self.env, fwd_pos)

	return False

	@property
	def dir_vec(self):
	"""
	Get the direction vector for the agent, pointing in the direction
	of forward movement.
	"""

	assert self.npc_dir >= 0 and self.npc_dir < 4
	return DIR_TO_VEC[self.npc_dir]

	@property
	def right_vec(self):
	"""
	Get the vector pointing to the right of the agent.
	"""

	dx, dy = self.dir_vec
	return np.array((-dy, dx))


	@property
	def front_pos(self):
	"""
	Get the position of the cell that is right in front of the agent
	"""

	return self.cur_pos + self.dir_vec

	@property
	def back_pos(self):
	"""
	Get the position of the cell that is right in front of the agent
	"""

	return self.cur_pos - self.dir_vec

	@property
	def right_pos(self):
	"""
	Get the position of the cell that is right in front of the agent
	"""

	return self.cur_pos + self.right_vec

	@property
	def left_pos(self):
	"""
	Get the position of the cell that is right in front of the agent
	"""

	return self.cur_pos - self.right_vec

	def draw_npc_face(self, c):
	assert self.npc_type == 0

	assert all(COLORS[self.color] == c)

	shapes = []
	shapes_colors = []

	# Draw eyes
	shapes.append(point_in_circle(cx=0.70, cy=0.50, r=0.10))
	shapes_colors.append(c)
	shapes.append(point_in_circle(cx=0.30, cy=0.50, r=0.10))
	shapes_colors.append(c)

	# Draw mouth
	shapes.append(point_in_rect(0.20, 0.80, 0.72, 0.81))
	shapes_colors.append(c)

	# Draw bottom hat
	shapes.append(point_in_triangle((0.15, 0.28),
	(0.85, 0.28),
	(0.50, 0.05)))
	shapes_colors.append(c)
	# Draw top hat
	shapes.append(point_in_rect(0.30, 0.70, 0.05, 0.28))
	shapes_colors.append(c)
	return shapes, shapes_colors

	def render(self, img):


	c = COLORS[self.color]

	npc_shapes = []
	npc_shapes_colors = []


	npc_face_shapes, npc_face_shapes_colors = self.draw_npc_face(c=c)

	npc_shapes.extend(npc_face_shapes)
	npc_shapes_colors.extend(npc_face_shapes_colors)

	if hasattr(self, "npc_dir"):
	# Pre-rotation to ensure npc_dir = 1 means NPC looks downwards
	npc_shapes = [rotate_fn(v, cx=0.5, cy=0.5, theta=-1*(math.pi / 2)) for v in npc_shapes]
	# Rotate npc based on its direction
	npc_shapes = [rotate_fn(v, cx=0.5, cy=0.5, theta=(math.pi/2) * self.npc_dir) for v in npc_shapes]

	if hasattr(self, "point_dir"):
	if self.is_pointing():
	# default points east
	finger = point_in_triangle((0.85, 0.1),
	(0.85, 0.3),
	(0.99, 0.2))
	finger = rotate_fn(finger, cx=0.5, cy=0.5, theta=(math.pi/2) * self.point_dir)

	npc_shapes.append(finger)
	npc_shapes_colors.append(c)

	if self.last_action == self.toggle_action.__name__:
	# T symbol
	t_symbol = [point_in_rect(0.8, 0.84, 0.02, 0.18), point_in_rect(0.8, 0.95, 0.08, 0.12)]
	t_symbol = [rotate_fn(v, cx=0.5, cy=0.5, theta=(math.pi/2) * self.npc_dir) for v in t_symbol]
	npc_shapes.extend(t_symbol)
	npc_shapes_colors.extend([c, c])

	elif self.last_action == self.go_forward.__name__:
	# symbol for Forward (ommited for speed)
	pass

	if self.env.hidden_npc:
	# crossed eye symbol
	dx, dy = 0.15, -0.2

	# draw eye
	npc_shapes.append(point_in_circle(cx=0.70+dx, cy=0.48+dy, r=0.11))
	npc_shapes_colors.append((128,128,128))

	npc_shapes.append(point_in_circle(cx=0.30+dx, cy=0.52+dy, r=0.11))
	npc_shapes_colors.append((128,128,128))

	npc_shapes.append(point_in_circle(0.5+dx, 0.5+dy, 0.25))
	npc_shapes_colors.append((128, 128, 128))

	npc_shapes.append(point_in_circle(0.5+dx, 0.5+dy, 0.20))
	npc_shapes_colors.append((0, 0, 0))

	npc_shapes.append(point_in_circle(0.5+dx, 0.5+dy, 0.1))
	npc_shapes_colors.append((128, 128, 128))

	# cross it
	npc_shapes.append(point_in_line(0.2+dx, 0.7+dy, 0.8+dx, 0.3+dy, 0.04))
	npc_shapes_colors.append((128, 128, 128))


	# Draw shapes
	for v, c in zip(npc_shapes, npc_shapes_colors):
	fill_coords(img, v, c)

	def cache(self, args, *kwargs):
	"""Used for cached rendering."""
	# adding npc_dir and point_dir because, when egocentric coordinates are used,
	# they can result in the same encoding but require new rendering
	return self.encode(args, *kwargs) + (self.npc_dir, self.point_dir,)

	def can_overlap(self):
	# If the NPC is hidden, agent can overlap on it
	return self.env.hidden_npc

	def encode(self, nb_dims=3, absolute_coordinates=False):
	if not hasattr(self, "npc_type"):
	raise ValueError("An NPC class must implement the npc_type (int)")

	if not hasattr(self, "env"):
	raise ValueError("An NPC class must have the env")

	assert nb_dims == 6+2*bool(absolute_coordinates)

	if self.env.hidden_npc:
	return (1,) + (0,) * (nb_dims-1)

	assert self.env.egocentric_observation == (not absolute_coordinates)

	if absolute_coordinates:
	v = (OBJECT_TO_IDX[self.type], *self.cur_pos, COLOR_TO_IDX[self.color], self.npc_type)
	else:
	v = (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], self.npc_type)

	if self.env.add_npc_direction:
	assert hasattr(self, "npc_dir"), "4D but there is no npc dir in NPC state"
	assert self.npc_dir >= 0

	if self.env.egocentric_observation:
	assert self.env.agent_dir >= 0

	# 0 - eye contact; 2 - gaze in same direction; 1 - to left; 3 - to right
	npc_dir_enc = (self.npc_dir - self.env.agent_dir + 2) % 4

	v += (npc_dir_enc,)
	else:
	v += (self.npc_dir,)

	if self.env.add_npc_point_direction:
	assert hasattr(self, "point_dir"), "5D but there is no npc point dir in NPC state"

	if self.point_dir == 255:
	# no pointing
	v += (self.point_dir,)

	elif 0 <= self.point_dir <= 3:
	# pointing

	if self.env.egocentric_observation:
	assert self.env.agent_dir >= 0

	# 0 - pointing at agent; 2 - point in direction of agent gaze; 1 - to left; 3 - to right
	point_enc = (self.point_dir - self.env.agent_dir + 2) % 4
	v += (point_enc,)

	else:
	v += (self.point_dir,)

	else:
	raise ValueError(f"Undefined point direction {self.point_dir}")

	if self.env.add_npc_last_prim_action:
	assert hasattr(self, "last_action"), "6D but there is no last action in NPC state"

	if self.last_action in ["point_E", "point_S", "point_W", "point_N"] and self.env.egocentric_observation:

	# get the direction of the last point
	last_action_point_dir = point_dir_encoding[self.last_action]

	# convert to relative dir
	# 0 - pointing at agent; 2 - point in direction of agent gaze; 1 - to left; 3 - to right
	last_action_relative_point_dir = (last_action_point_dir - self.env.agent_dir + 2) % 4

	# the point_X action ids are in range [point_E, ... , point_N]
	# id of point_E is the starting one, we use the same range [E, S, W ,N ] -> [at, left, same, right]
	last_action_id = self.prim_actions_dict["point_E"] + last_action_relative_point_dir

	else:
	last_action_id = self.prim_actions_dict[self.last_action]

	v += (last_action_id,)

	assert self.point_dir >= 0
	assert len(v) == nb_dims

	return v


	class Grid:
	"""
	Represent a grid and operations on it
	"""

	# Static cache of pre-renderer tiles
	tile_cache = {}

	def __init__(self, width, height, nb_obj_dims):
	assert width >= 3
	assert height >= 3

	self.width = width
	self.height = height
	self.nb_obj_dims = nb_obj_dims

	self.grid = [None] * width * height

	def __contains__(self, key):
	if isinstance(key, WorldObj):
	for e in self.grid:
	if e is key:
	return True
	elif isinstance(key, tuple):
	for e in self.grid:
	if e is None:
	continue
	if (e.color, e.type) == key:
	return True
	if key[0] is None and key[1] == e.type:
	return True
	return False

	def __eq__(self, other):
	grid1 = self.encode()
	grid2 = other.encode()
	return np.array_equal(grid2, grid1)

	def __ne__(self, other):
	return not self == other

	def copy(self):
	from copy import deepcopy
	return deepcopy(self)

	def set(self, i, j, v):
	assert i >= 0 and i < self.width
	assert j >= 0 and j < self.height
	self.grid[j * self.width + i] = v

	def get(self, i, j):
	assert i >= 0 and i < self.width
	assert j >= 0 and j < self.height
	return self.grid[j * self.width + i]

	def horz_wall(self, x, y, length=None, obj_type=Wall):
	if length is None:
	length = self.width - x
	for i in range(0, length):
	o = obj_type()
	o.cur_pos = np.array((x+i, y))
	self.set(x + i, y, o)

	def vert_wall(self, x, y, length=None, obj_type=Wall):
	if length is None:
	length = self.height - y
	for j in range(0, length):
	o = obj_type()
	o.cur_pos = np.array((x, y+j))
	self.set(x, y + j, o)

	def wall_rect(self, x, y, w, h):
	self.horz_wall(x, y, w)
	self.horz_wall(x, y+h-1, w)
	self.vert_wall(x, y, h)
	self.vert_wall(x+w-1, y, h)

	def rotate_left(self):
	"""
	Rotate the grid to the left (counter-clockwise)
	"""

	grid = Grid(self.height, self.width, self.nb_obj_dims)

	for i in range(self.width):
	for j in range(self.height):
	v = self.get(i, j)
	grid.set(j, grid.height - 1 - i, v)

	return grid

	def slice(self, topX, topY, width, height):
	"""
	Get a subset of the grid
	"""

	grid = Grid(width, height, self.nb_obj_dims)

	for j in range(0, height):
	for i in range(0, width):
	x = topX + i
	y = topY + j

	if x >= 0 and x < self.width and \
	y >= 0 and y < self.height:
	v = self.get(x, y)
	else:
	v = Wall()

	grid.set(i, j, v)

	return grid

	@classmethod
	def render_tile(
	cls,
	obj,
	agent_dir=None,
	highlight=False,
	tile_size=TILE_PIXELS,
	subdivs=3,
	nb_obj_dims=3,
	mask_unobserved=False
	):
	"""
	Render a tile and cache the result
	"""
	# Hash map lookup key for the cache
	key = (agent_dir, highlight, tile_size, mask_unobserved)
	# key = obj.encode(nb_dims=nb_obj_dims) + key if obj else key
	key = obj.cache(nb_dims=nb_obj_dims) + key if obj else key

	if key in cls.tile_cache:
	return cls.tile_cache[key]

	img = np.zeros(shape=(tile_size * subdivs, tile_size * subdivs, 3), dtype=np.uint8) # 3D for rendering

	# Draw the grid lines (top and left edges)
	fill_coords(img, point_in_rect(0, 0.031, 0, 1), (100, 100, 100))
	fill_coords(img, point_in_rect(0, 1, 0, 0.031), (100, 100, 100))

	if obj != None:
	obj.render(img)

	# Overlay the agent on top
	if agent_dir is not None:
	tri_fn = point_in_triangle(
	(0.12, 0.19),
	(0.87, 0.50),
	(0.12, 0.81),
	)

	# Rotate the agent based on its direction
	tri_fn = rotate_fn(tri_fn, cx=0.5, cy=0.5, theta=0.5math.piagent_dir)
	fill_coords(img, tri_fn, (255, 0, 0))

	# Highlight the cell if needed
	if highlight:
	highlight_img(img)
	elif mask_unobserved:
	# mask unobserved and not highlighted -> unobserved by the agent
	img *= 0

	# Downsample the image to perform supersampling/anti-aliasing
	img = downsample(img, subdivs)

	# Cache the rendered tile
	cls.tile_cache[key] = img

	return img

	def render(
	self,
	tile_size,
	agent_pos=None,
	agent_dir=None,
	highlight_mask=None,
	mask_unobserved=False,
	):
	"""
	Render this grid at a given scale
	:param r: target renderer object
	:param tile_size: tile size in pixels
	"""

	if highlight_mask is None:
	highlight_mask = np.zeros(shape=(self.width, self.height), dtype=np.bool)

	# Compute the total grid size
	width_px = self.width * tile_size
	height_px = self.height * tile_size
	img = np.zeros(shape=(height_px, width_px, 3), dtype=np.uint8)

	# Render the grid
	for j in range(0, self.height):
	for i in range(0, self.width):
	cell = self.get(i, j)

	agent_here = np.array_equal(agent_pos, (i, j))
	tile_img = Grid.render_tile(
	cell,
	agent_dir=agent_dir if agent_here else None,
	highlight=highlight_mask[i, j],
	tile_size=tile_size,
	nb_obj_dims=self.nb_obj_dims,
	mask_unobserved=mask_unobserved
	)

	ymin = j * tile_size
	ymax = (j+1) * tile_size
	xmin = i * tile_size
	xmax = (i+1) * tile_size
	img[ymin:ymax, xmin:xmax, :] = tile_img

	return img

	def encode(self, vis_mask=None, absolute_coordinates=False):
	"""
	Produce a compact numpy encoding of the grid
	"""

	if vis_mask is None:
	vis_mask = np.ones((self.width, self.height), dtype=bool)

	array = np.zeros((self.width, self.height, self.nb_obj_dims), dtype='uint8')

	for i in range(self.width):
	for j in range(self.height):
	if vis_mask[i, j]:
	v = self.get(i, j)

	if v is None:
	array[i, j, 0] = OBJECT_TO_IDX['empty']
	array[i, j, 1:] = 0

	else:
	array[i, j, :] = v.encode(nb_dims=self.nb_obj_dims, absolute_coordinates=absolute_coordinates)

	return array

	@staticmethod
	def decode(array):
	"""
	Decode an array grid encoding back into a grid
	"""

	width, height, channels = array.shape
	assert channels in [5, 4, 3]

	vis_mask = np.ones(shape=(width, height), dtype=np.bool)

	grid = Grid(width, height, nb_obj_dims=channels)
	for i in range(width):
	for j in range(height):
	if len(array[i, j]) == 3:
	type_idx, color_idx, state = array[i, j]
	else:
	type_idx, color_idx, state, orient = array[i, j]

	v = WorldObj.decode(type_idx, color_idx, state)
	grid.set(i, j, v)
	vis_mask[i, j] = (type_idx != OBJECT_TO_IDX['unseen'])

	return grid, vis_mask

	def process_vis(grid, agent_pos):
	# mask = np.zeros(shape=(grid.width, grid.height), dtype=np.bool)
	#
	# mask[agent_pos[0], agent_pos[1]] = True
	#
	# for j in reversed(range(0, grid.height)):
	# for i in range(0, grid.width-1):
	# if not mask[i, j]:
	# continue
	#
	# cell = grid.get(i, j)
	# if cell and not cell.see_behind():
	# continue
	#
	# mask[i+1, j] = True
	# if j > 0:
	# mask[i+1, j-1] = True
	# mask[i, j-1] = True
	#
	# for i in reversed(range(1, grid.width)):
	# if not mask[i, j]:
	# continue
	#
	# cell = grid.get(i, j)
	# if cell and not cell.see_behind():
	# continue
	#
	# mask[i-1, j] = True
	# if j > 0:
	# mask[i-1, j-1] = True
	# mask[i, j-1] = True

	mask = np.ones(shape=(grid.width, grid.height), dtype=np.bool)
	# handle frontal occlusions

	# 45 deg
	for j in reversed(range(0, agent_pos[1]+1)):
	dy = abs(agent_pos[1] - j)

	# in front of the agent
	i = agent_pos[0]
	cell = grid.get(i, j)
	if (cell and not cell.see_behind()) or mask[i, j] == False:

	if j < agent_pos[1] and j > 0:
	# 45 deg
	mask[i-1,j-1] = False
	mask[i,j-1] = False
	mask[i+1,j-1] = False

	# agent -> to the left
	for i in reversed(range(1, agent_pos[0])):
	dx = abs(agent_pos[0] - i)
	cell = grid.get(i, j)

	if (cell and not cell.see_behind()) or mask[i,j] == False:
	# angle
	if dx >= dy:
	mask[i - 1, j] = False

	if j > 0:
	mask[i - 1, j - 1] = False
	if dy >= dx:
	mask[i, j - 1] = False

	# agent -> to the right
	for i in range(agent_pos[0]+1, grid.width-1):
	dx = abs(agent_pos[0] - i)
	cell = grid.get(i, j)

	if (cell and not cell.see_behind()) or mask[i,j] == False:
	# angle
	if dx >= dy:
	mask[i + 1, j] = False

	if j > 0:
	mask[i + 1, j - 1] = False
	if dy >= dx:
	mask[i, j - 1] = False

	# for i in range(0, grid.width):
	# cell = grid.get(i, j)
	# if (cell and not cell.see_behind()) or mask[i,j] == False:
	# mask[i, j-1] = False

	# grid
	# for j in reversed(range(0, agent_pos[1]+1)):
	#
	# i = agent_pos[0]
	# cell = grid.get(i, j)
	# if (cell and not cell.see_behind()) or mask[i, j] == False:
	# if j < agent_pos[1]:
	# # grid
	# mask[i,j-1] = False
	#
	# for i in reversed(range(1, agent_pos[0])):
	# # agent -> to the left
	# cell = grid.get(i, j)
	# if (cell and not cell.see_behind()) or mask[i,j] == False:
	# # grid
	# mask[i-1, j] = False
	# if j < agent_pos[1] and j > 0:
	# mask[i, j-1] = False
	#
	# for i in range(agent_pos[0]+1, grid.width-1):
	# # agent -> to the right
	# cell = grid.get(i, j)
	# if (cell and not cell.see_behind()) or mask[i,j] == False:
	# # grid
	# mask[i+1, j] = False
	# if j < agent_pos[1] and j > 0:
	# mask[i, j-1] = False

	for j in range(0, grid.height):
	for i in range(0, grid.width):
	if not mask[i, j]:
	grid.set(i, j, None)

	return mask


	class MiniGridEnv(gym.Env):
	"""
	2D grid world game environment
	"""

	metadata = {
	'render.modes': ['human', 'rgb_array'],
	'video.frames_per_second' : 10
	}

	# Enumeration of possible actions
	class Actions(IntEnum):
	# Turn left, turn right, move forward
	left = 0
	right = 1
	forward = 2

	# Pick up an object
	pickup = 3
	# Drop an object
	drop = 4
	# Toggle/activate an object
	toggle = 5

	# Done completing task
	done = 6

	def __init__(
	self,
	grid_size=None,
	width=None,
	height=None,
	max_steps=100,
	see_through_walls=False,
	full_obs=False,
	seed=None,
	agent_view_size=7,
	actions=None,
	action_space=None,
	add_npc_direction=False,
	add_npc_point_direction=False,
	add_npc_last_prim_action=False,
	reward_diminish_factor=0.9,
	egocentric_observation=True,
	):

	# sanity check params for SocialAI experiments
	if "SocialAI" in type(self).__name__:
	assert egocentric_observation
	assert grid_size == 10
	assert not see_through_walls
	assert max_steps == 80
	assert agent_view_size == 7
	assert not full_obs
	assert add_npc_direction and add_npc_point_direction and add_npc_last_prim_action

	self.egocentric_observation = egocentric_observation

	if hasattr(self, "lever_active_steps"):
	assert self.lever_active_steps == 10

	# Can't set both grid_size and width/height
	if grid_size:
	assert width == None and height == None
	width = grid_size
	height = grid_size

	# Action enumeration for this environment
	if actions:
	self.actions = actions
	else:
	self.actions = MiniGridEnv.Actions

	# Actions are discrete integer values
	if action_space:
	self.action_space = action_space
	else:
	self.action_space = spaces.MultiDiscrete([len(self.actions)])

	# Number of cells (width and height) in the agent view
	assert agent_view_size % 2 == 1
	assert agent_view_size >= 3
	self.agent_view_size = agent_view_size

	# Number of object dimensions (i.e. number of channels in symbolic image)
	self.add_npc_direction = add_npc_direction
	self.add_npc_point_direction = add_npc_point_direction
	self.add_npc_last_prim_action = add_npc_last_prim_action
	self.nb_obj_dims = 3 + 2*bool(not self.egocentric_observation) + int(self.add_npc_direction) + int(self.add_npc_point_direction) + int(self.add_npc_last_prim_action)

	# Observations are dictionaries containing an
	# encoding of the grid and a textual 'mission' string
	self.observation_space = spaces.Box(
	low=0,
	high=255,
	shape=(self.agent_view_size, self.agent_view_size, self.nb_obj_dims),
	dtype='uint8'
	)
	self.observation_space = spaces.Dict({
	'image': self.observation_space
	})

	# Range of possible rewards
	self.reward_range = (0, 1)

	# Window to use for human rendering mode
	self.window = None

	# Environment configuration
	self.width = width
	self.height = height
	self.max_steps = max_steps
	self.see_through_walls = see_through_walls
	self.full_obs = full_obs

	self.reward_diminish_factor = reward_diminish_factor

	# Current position and direction of the agent
	self.agent_pos = None
	self.agent_dir = None

	# Initialize the RNG
	self.seed(seed=seed)

	# Initialize the state
	self.reset()

	def reset(self):
	# Current position and direction of the agent
	self.agent_pos = None
	self.agent_dir = None

	# Generate a new random grid at the start of each episode
	# To keep the same grid for each episode, call env.seed() with
	# the same seed before calling env.reset()
	self._gen_grid(self.width, self.height)

	# These fields should be defined by _gen_grid
	assert self.agent_pos is not None
	assert self.agent_dir is not None

	# Check that the agent doesn't overlap with an object
	start_cell = self.grid.get(*self.agent_pos)
	assert start_cell is None or start_cell.can_overlap()

	# Item picked up, being carried, initially nothing
	self.carrying = None

	# Step count since episode start
	self.step_count = 0

	# Return first observation
	obs = self.gen_obs(full_obs=self.full_obs)
	return obs

	def reset_with_info(self, args, *kwargs):
	obs = self.reset(args, *kwargs)
	info = self.generate_info(done=False, reward=0)
	return obs, info

	def seed(self, seed=1337):
	# Seed the random number generator
	self.np_random, _ = seeding.np_random(seed)
	return [seed]

	def hash(self, size=16):
	"""Compute a hash that uniquely identifies the current state of the environment.
	:param size: Size of the hashing
	"""
	sample_hash = hashlib.sha256()

	to_encode = [self.grid.encode(), self.agent_pos, self.agent_dir]
	for item in to_encode:
	sample_hash.update(str(item).encode('utf8'))

	return sample_hash.hexdigest()[:size]

	@property
	def steps_remaining(self):
	return self.max_steps - self.step_count

	def is_near(self, pos1, pos2):
	ax, ay = pos1
	wx, wy = pos2
	if (ax == wx and abs(ay - wy) == 1) or (ay == wy and abs(ax - wx) == 1):
	return True
	return False

	def get_cell(self, x, y):
	return self.grid.get(x, y)

	def __str__(self):
	"""
	Produce a pretty string of the environment's grid along with the agent.
	A grid cell is represented by 2-character string, the first one for
	the object and the second one for the color.
	"""

	# Map of object types to short string
	OBJECT_TO_STR = {
	'wall' : 'W',
	'floor' : 'F',
	'door' : 'D',
	'key' : 'K',
	'ball' : 'A',
	'box' : 'B',
	'goal' : 'G',
	'lava' : 'V',
	}

	# Short string for opened door
	OPENDED_DOOR_IDS = '_'

	# Map agent's direction to short string
	AGENT_DIR_TO_STR = {
	0: '>',
	1: 'V',
	2: '<',
	3: '^'
	}

	str = ''

	for j in range(self.grid.height):

	for i in range(self.grid.width):
	if i == self.agent_pos[0] and j == self.agent_pos[1]:
	str += 2 * AGENT_DIR_TO_STR[self.agent_dir]
	continue

	c = self.grid.get(i, j)

	if c == None:
	str += ' '
	continue

	if c.type == 'door':
	if c.is_open:
	str += '__'
	elif c.is_locked:
	str += 'L' + c.color[0].upper()
	else:
	str += 'D' + c.color[0].upper()
	continue

	str += OBJECT_TO_STR[c.type] + c.color[0].upper()

	if j < self.grid.height - 1:
	str += '\n'

	return str

	def _gen_grid(self, width, height):
	assert False, "_gen_grid needs to be implemented by each environment"

	def _reward(self):
	"""
	Compute the reward to be given upon success
	"""

	return 1 - self.reward_diminish_factor * (self.step_count / self.max_steps)

	def _rand_int(self, low, high):
	"""
	Generate random integer in [low,high[
	"""
	return self.np_random.randint(low, high)

	def _rand_float(self, low, high):
	"""
	Generate random float in [low,high[
	"""

	return self.np_random.uniform(low, high)

	def _rand_bool(self):
	"""
	Generate random boolean value
	"""

	return (self.np_random.randint(0, 2) == 0)

	def _rand_elem(self, iterable):
	"""
	Pick a random element in a list
	"""

	lst = list(iterable)
	idx = self._rand_int(0, len(lst))
	return lst[idx]

	def _rand_subset(self, iterable, num_elems):
	"""
	Sample a random subset of distinct elements of a list
	"""

	lst = list(iterable)
	assert num_elems <= len(lst)

	out = []

	while len(out) < num_elems:
	elem = self._rand_elem(lst)
	lst.remove(elem)
	out.append(elem)

	return out

	def _rand_color(self):
	"""
	Generate a random color name (string)
	"""

	return self._rand_elem(COLOR_NAMES)

	def _rand_pos(self, xLow, xHigh, yLow, yHigh):
	"""
	Generate a random (x,y) position tuple
	"""

	return (
	self.np_random.randint(xLow, xHigh),
	self.np_random.randint(yLow, yHigh)
	)

	def find_loc(self,
	top=None,
	size=None,
	reject_fn=None,
	max_tries=math.inf,
	reject_agent_pos=True,
	reject_taken_pos=True
	):
	"""
	Place an object at an empty position in the grid

	:param top: top-left position of the rectangle where to place
	:param size: size of the rectangle where to place
	:param reject_fn: function to filter out potential positions
	"""

	if top is None:
	top = (0, 0)
	else:
	top = (max(top[0], 0), max(top[1], 0))

	if size is None:
	size = (self.grid.width, self.grid.height)

	num_tries = 0

	while True:
	# This is to handle with rare cases where rejection sampling
	# gets stuck in an infinite loop
	if num_tries > max_tries:
	raise RecursionError('rejection sampling failed in place_obj')
	if num_tries % 10000 == 0 and num_tries > 0:
	warnings.warn("num_tries = {}. This is probably an infinite loop. {}".format(num_tries, get_traceback()))
	# warnings.warn("num_tries = {}. This is probably an infinite loop.".format(num_tries))
	exit()
	break

	num_tries += 1

	pos = np.array((
	self._rand_int(top[0], min(top[0] + size[0], self.grid.width)),
	self._rand_int(top[1], min(top[1] + size[1], self.grid.height))
	))

	# Don't place the object on top of another object
	if reject_taken_pos:
	if self.grid.get(*pos) != None:
	continue

	# Don't place the object where the agent is
	if reject_agent_pos and np.array_equal(pos, self.agent_pos):
	continue

	# Check if there is a filtering criterion
	if reject_fn and reject_fn(self, pos):
	continue

	break

	return pos

	def place_obj(self,
	obj,
	top=None,
	size=None,
	reject_fn=None,
	max_tries=math.inf
	):
	"""
	Place an object at an empty position in the grid

	:param top: top-left position of the rectangle where to place
	:param size: size of the rectangle where to place
	:param reject_fn: function to filter out potential positions
	"""

	# if top is None:
	# top = (0, 0)
	# else:
	# top = (max(top[0], 0), max(top[1], 0))
	#
	# if size is None:
	# size = (self.grid.width, self.grid.height)
	#
	# num_tries = 0
	#
	# while True:
	# # This is to handle with rare cases where rejection sampling
	# # gets stuck in an infinite loop
	# if num_tries > max_tries:
	# raise RecursionError('rejection sampling failed in place_obj')
	#
	# num_tries += 1
	#
	# pos = np.array((
	# self._rand_int(top[0], min(top[0] + size[0], self.grid.width)),
	# self._rand_int(top[1], min(top[1] + size[1], self.grid.height))
	# ))
	#
	# # Don't place the object on top of another object
	# if self.grid.get(*pos) != None:
	# continue
	#
	# # Don't place the object where the agent is
	# if np.array_equal(pos, self.agent_pos):
	# continue
	#
	# # Check if there is a filtering criterion
	# if reject_fn and reject_fn(self, pos):
	# continue
	#
	# break
	#
	# self.grid.set(*pos, obj)
	#
	# if obj is not None:
	# obj.init_pos = pos
	# obj.cur_pos = pos
	#
	# return pos

	pos = self.find_loc(
	top=top,
	size=size,
	reject_fn=reject_fn,
	max_tries=max_tries
	)

	if obj is None:
	self.grid.set(*pos, obj)
	else:
	self.put_obj_np(obj, pos)

	return pos

	def put_obj_np(self, obj, pos):
	"""
	Put an object at a specific position in the grid
	"""

	assert isinstance(pos, np.ndarray)

	i, j = pos

	cell = self.grid.get(i, j)
	if cell is not None:
	raise ValueError("trying to put {} on {}".format(obj, cell))

	self.grid.set(i, j, obj)
	obj.init_pos = np.array((i, j))
	obj.cur_pos = np.array((i, j))

	def put_obj(self, obj, i, j):
	"""
	Put an object at a specific position in the grid
	"""
	warnings.warn(
	"This function is kept for backwards compatiblity with minigrid. It is recommended to use put_object_np()."
	)
	raise DeprecationWarning("Deprecated use put_obj_np. (or remove this Warning)")

	self.grid.set(i, j, obj)
	obj.init_pos = (i, j)
	obj.cur_pos = (i, j)

	def place_agent(
	self,
	top=None,
	size=None,
	rand_dir=True,
	max_tries=math.inf
	):
	"""
	Set the agent's starting point at an empty position in the grid
	"""

	self.agent_pos = None
	pos = self.place_obj(None, top, size, max_tries=max_tries)
	self.agent_pos = pos

	if rand_dir:
	self.agent_dir = self._rand_int(0, 4)

	return pos

	@property
	def dir_vec(self):
	"""
	Get the direction vector for the agent, pointing in the direction
	of forward movement.
	"""
	assert self.agent_dir >= 0 and self.agent_dir < 4
	return DIR_TO_VEC[self.agent_dir]

	@property
	def right_vec(self):
	"""
	Get the vector pointing to the right of the agent.
	"""

	dx, dy = self.dir_vec
	return np.array((-dy, dx))

	@property
	def front_pos(self):
	"""
	Get the position of the cell that is right in front of the agent
	"""

	return self.agent_pos + self.dir_vec

	@property
	def back_pos(self):
	"""
	Get the position of the cell that is right in front of the agent
	"""

	return self.agent_pos - self.dir_vec

	@property
	def right_pos(self):
	"""
	Get the position of the cell that is right in front of the agent
	"""

	return self.agent_pos + self.right_vec

	@property
	def left_pos(self):
	"""
	Get the position of the cell that is right in front of the agent
	"""

	return self.agent_pos - self.right_vec

	def get_view_coords(self, i, j):
	"""
	Translate and rotate absolute grid coordinates (i, j) into the
	agent's partially observable view (sub-grid). Note that the resulting
	coordinates may be negative or outside of the agent's view size.
	"""

	ax, ay = self.agent_pos
	dx, dy = self.dir_vec
	rx, ry = self.right_vec

	# Compute the absolute coordinates of the top-left view corner
	sz = self.agent_view_size
	hs = self.agent_view_size // 2
	tx = ax + (dx * (sz-1)) - (rx * hs)
	ty = ay + (dy * (sz-1)) - (ry * hs)

	lx = i - tx
	ly = j - ty

	# Project the coordinates of the object relative to the top-left
	# corner onto the agent's own coordinate system
	vx = (rxlx + ryly)
	vy = -(dxlx + dyly)

	return vx, vy

	def get_view_exts(self, dir=None, view_size=None, pos=None):
	"""
	Get the extents of the square set of tiles visible to the agent (or to an npc if specified
	Note: the bottom extent indices are not included in the set
	"""

	# by default compute view exts for agent
	if (dir is None) and (view_size is None) and (pos is None):
	dir = self.agent_dir
	view_size = self.agent_view_size
	pos = self.agent_pos

	# Facing right
	if dir == 0:
	topX = pos[0]
	topY = pos[1] - view_size // 2
	# Facing down
	elif dir == 1:
	topX = pos[0] - view_size // 2
	topY = pos[1]
	# Facing left
	elif dir == 2:
	topX = pos[0] - view_size + 1
	topY = pos[1] - view_size // 2
	# Facing up
	elif dir == 3:
	topX = pos[0] - view_size // 2
	topY = pos[1] - view_size + 1
	else:
	assert False, "invalid agent direction: {}".format(dir)

	botX = topX + view_size
	botY = topY + view_size

	return (topX, topY, botX, botY)

	def relative_coords(self, x, y):
	"""
	Check if a grid position belongs to the agent's field of view, and returns the corresponding coordinates
	"""

	vx, vy = self.get_view_coords(x, y)

	if vx < 0 or vy < 0 or vx >= self.agent_view_size or vy >= self.agent_view_size:
	return None

	return vx, vy

	def in_view(self, x, y):
	"""
	check if a grid position is visible to the agent
	"""

	return self.relative_coords(x, y) is not None

	def agent_sees(self, x, y):
	"""
	Check if a non-empty grid position is visible to the agent
	"""

	coordinates = self.relative_coords(x, y)
	if coordinates is None:
	return False
	vx, vy = coordinates
	assert not self.full_obs, "agent sees function not implemented with full_obs"
	obs = self.gen_obs()
	obs_grid, _ = Grid.decode(obs['image'])
	obs_cell = obs_grid.get(vx, vy)
	world_cell = self.grid.get(x, y)

	return obs_cell is not None and obs_cell.type == world_cell.type

	def step(self, action):
	self.step_count += 1

	reward = 0
	done = False

	# Get the position in front of the agent
	fwd_pos = self.front_pos

	# Get the contents of the cell in front of the agent
	fwd_cell = self.grid.get(*fwd_pos)

	# Rotate left
	if action == self.actions.left:
	self.agent_dir -= 1
	if self.agent_dir < 0:
	self.agent_dir += 4

	# Rotate right
	elif action == self.actions.right:
	self.agent_dir = (self.agent_dir + 1) % 4

	# Move forward
	elif action == self.actions.forward:
	if fwd_cell != None and fwd_cell.can_push():
	fwd_cell.push(push_dir=self.agent_dir, pusher="agent")

	if fwd_cell == None or fwd_cell.can_overlap():
	self.agent_pos = fwd_pos
	if fwd_cell != None and fwd_cell.type == 'goal':
	done = True
	reward = self._reward()
	if fwd_cell != None and fwd_cell.type == 'lava':
	done = True

	# Pick up an object
	elif hasattr(self.actions, "pickup") and action == self.actions.pickup:
	if fwd_cell and fwd_cell.can_pickup():
	if self.carrying is None:
	self.carrying = fwd_cell
	self.carrying.cur_pos = np.array([-1, -1])
	self.grid.set(*fwd_pos, None)

	# Drop an object
	elif hasattr(self.actions, "drop") and action == self.actions.drop:
	if not fwd_cell and self.carrying:
	self.grid.set(*fwd_pos, self.carrying)
	self.carrying.cur_pos = fwd_pos
	self.carrying = None

	# Toggle/activate an object
	elif action == self.actions.toggle:
	if fwd_cell:
	fwd_cell.toggle(self, fwd_pos)

	# Done action (not used by default)
	elif action == self.actions.done:
	pass

	elif action in map(int, self.actions):
	# action that was added in an inheriting class (ex. talk action)
	pass

	elif np.isnan(action):
	# action skip
	pass

	else:
	assert False, f"unknown action {action}"

	if self.step_count >= self.max_steps:
	done = True

	obs = self.gen_obs(full_obs=self.full_obs)

	info = self.generate_info(done, reward)

	return obs, reward, done, info

	def generate_info(self, done, reward):

	success = done and reward > 0

	info = {"success": success}

	gen_extra_info_dict = self.gen_extra_info() # add stuff needed for textual observations here

	assert not any(item in info for item in gen_extra_info_dict), "Duplicate keys found with gen_extra_info"

	info = {
	**info,
	**gen_extra_info_dict,
	}
	return info

	def gen_extra_info(self):
	grid, vis_mask = self.gen_obs_grid()
	carrying = self.carrying
	agent_pos_vx, agent_pos_vy = self.get_view_coords(self.agent_pos[0], self.agent_pos[1])
	npc_actions_dict = SocialAINPCActionsDict

	extra_info = {
	"image": grid.encode(vis_mask),
	"vis_mask": vis_mask,
	"carrying": carrying,
	"agent_pos_vx": agent_pos_vx,
	"agent_pos_vy": agent_pos_vy,
	"npc_actions_dict": npc_actions_dict
	}
	return extra_info

	def gen_obs_grid(self):
	"""
	Generate the sub-grid observed by the agent.
	This method also outputs a visibility mask telling us which grid
	cells the agent can actually see.
	"""

	topX, topY, botX, botY = self.get_view_exts()

	grid = self.grid.slice(topX, topY, self.agent_view_size, self.agent_view_size)

	for i in range(self.agent_dir + 1):
	grid = grid.rotate_left()

	# Process occluders and visibility
	# Note that this incurs some performance cost
	if not self.see_through_walls:
	vis_mask = grid.process_vis(agent_pos=(self.agent_view_size // 2, self.agent_view_size - 1))
	else:
	vis_mask = np.ones(shape=(grid.width, grid.height), dtype=np.bool)

	# Make it so the agent sees what it's carrying
	# We do this by placing the carried object at the agent's position
	# in the agent's partially observable view
	agent_pos = grid.width // 2, grid.height - 1
	if self.carrying:
	grid.set(*agent_pos, self.carrying)
	else:
	grid.set(*agent_pos, None)

	return grid, vis_mask

	def add_agent_to_grid(self, image):
	"""
	Add agent to symbolic pixel image, used only for full observation
	"""
	ax, ay = self.agent_pos
	image[ax,ay] = [9,9,9,self.agent_dir] # could be made cleaner by creating an Agent_id (here we use Lava_id)
	return image

	def gen_obs(self, full_obs=False):
	"""
	Generate the agent's view (partially observable, low-resolution encoding)
	Fully observable view can be returned when full_obs is set to True
	"""
	if full_obs:
	image = self.add_agent_to_grid(self.grid.encode())

	else:
	grid, vis_mask = self.gen_obs_grid()

	# Encode the partially observable view into a numpy array
	image = grid.encode(vis_mask, absolute_coordinates=not self.egocentric_observation)

	assert hasattr(self, 'mission'), "environments must define a textual mission string"

	# Observations are dictionaries containing:
	# - an image (partially observable view of the environment)
	# - the agent's direction/orientation (acting as a compass)
	# - a textual mission string (instructions for the agent)
	obs = {
	'image': image,
	'direction': self.agent_dir,
	'mission': self.mission
	}

	return obs

	def get_obs_render(self, obs, tile_size=TILE_PIXELS//2):
	"""
	Render an agent observation for visualization
	"""

	grid, vis_mask = Grid.decode(obs)

	# Render the whole grid
	img = grid.render(
	tile_size,
	agent_pos=(self.agent_view_size // 2, self.agent_view_size - 1),
	agent_dir=3,
	highlight_mask=vis_mask
	)

	return img

	def render(self, mode='human', close=False, highlight=True, tile_size=TILE_PIXELS, mask_unobserved=False):
	"""
	Render the whole-grid human view
	"""
	if mode == 'human' and close:
	if self.window:
	self.window.close()
	return

	if mode == 'human' and not self.window:
	import gym_minigrid.window
	self.window = gym_minigrid.window.Window('gym_minigrid')
	self.window.show(block=False)

	# Compute which cells are visible to the agent
	_, vis_mask = self.gen_obs_grid()

	# Compute the world coordinates of the bottom-left corner
	# of the agent's view area
	f_vec = self.dir_vec
	r_vec = self.right_vec
	top_left = self.agent_pos + f_vec * (self.agent_view_size-1) - r_vec * (self.agent_view_size // 2)

	# Mask of which cells to highlight
	highlight_mask = np.zeros(shape=(self.width, self.height), dtype=np.bool)

	# For each cell in the visibility mask
	for vis_j in range(0, self.agent_view_size):
	for vis_i in range(0, self.agent_view_size):
	# If this cell is not visible, don't highlight it
	if not vis_mask[vis_i, vis_j]:
	continue

	# Compute the world coordinates of this cell
	abs_i, abs_j = top_left - (f_vec * vis_j) + (r_vec * vis_i)

	if abs_i < 0 or abs_i >= self.width:
	continue
	if abs_j < 0 or abs_j >= self.height:
	continue

	# Mark this cell to be highlighted
	highlight_mask[abs_i, abs_j] = True

	# Render the whole grid
	img = self.grid.render(
	tile_size,
	self.agent_pos,
	self.agent_dir,
	highlight_mask=highlight_mask if highlight else None,
	mask_unobserved=mask_unobserved
	)
	if mode == 'human':
	# self.window.set_caption(self.mission)
	self.window.show_img(img)

	return img

	def get_mission(self):
	return self.mission

	def close(self):
	if self.window:
	self.window.close()
	return

	def gen_text_obs(self):
	grid, vis_mask = self.gen_obs_grid()

	# Encode the partially observable view into a numpy array
	image = grid.encode(vis_mask)

	# (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], state)
	# State, 0: open, 1: closed, 2: locked
	IDX_TO_COLOR = dict(zip(COLOR_TO_IDX.values(), COLOR_TO_IDX.keys()))
	IDX_TO_OBJECT = dict(zip(OBJECT_TO_IDX.values(), OBJECT_TO_IDX.keys()))

	list_textual_descriptions = []

	if self.carrying is not None:
	list_textual_descriptions.append("You carry a {} {}".format(self.carrying.color, self.carrying.type))

	agent_pos_vx, agent_pos_vy = self.get_view_coords(self.agent_pos[0], self.agent_pos[1])

	view_field_dictionary = dict()

	for i in range(image.shape[0]):
	for j in range(image.shape[1]):
	if image[i][j][0] != 0 and image[i][j][0] != 1 and image[i][j][0] != 2:
	if i not in view_field_dictionary.keys():
	view_field_dictionary[i] = dict()
	view_field_dictionary[i][j] = image[i][j]
	else:
	view_field_dictionary[i][j] = image[i][j]

	# Find the wall if any
	# We describe a wall only if there is no objects between the agent and the wall in straight line

	# Find wall in front
	add_wall_descr = False
	if add_wall_descr:
	j = agent_pos_vy - 1
	object_seen = False
	while j >= 0 and not object_seen:
	if image[agent_pos_vx][j][0] != 0 and image[agent_pos_vx][j][0] != 1:
	if image[agent_pos_vx][j][0] == 2:
	list_textual_descriptions.append(
	f"A wall is {agent_pos_vy - j} steps in front of you. \n") # forward
	object_seen = True
	else:
	object_seen = True
	j -= 1
	# Find wall left
	i = agent_pos_vx - 1
	object_seen = False
	while i >= 0 and not object_seen:
	if image[i][agent_pos_vy][0] != 0 and image[i][agent_pos_vy][0] != 1:
	if image[i][agent_pos_vy][0] == 2:
	list_textual_descriptions.append(
	f"A wall is {agent_pos_vx - i} steps to the left. \n") # left
	object_seen = True
	else:
	object_seen = True
	i -= 1
	# Find wall right
	i = agent_pos_vx + 1
	object_seen = False
	while i < image.shape[0] and not object_seen:
	if image[i][agent_pos_vy][0] != 0 and image[i][agent_pos_vy][0] != 1:
	if image[i][agent_pos_vy][0] == 2:
	list_textual_descriptions.append(
	f"A wall is {i - agent_pos_vx} steps to the right. \n") # right
	object_seen = True
	else:
	object_seen = True
	i += 1

	# list_textual_descriptions.append("You see the following objects: ")
	# returns the position of seen objects relative to you
	for i in view_field_dictionary.keys():
	for j in view_field_dictionary[i].keys():
	if i != agent_pos_vx or j != agent_pos_vy:
	object = view_field_dictionary[i][j]

	front_dist = agent_pos_vy - j
	left_right_dist = i-agent_pos_vx

	loc_descr = ""
	if front_dist == 1 and left_right_dist == 0:
	loc_descr += "Right in front of you "

	elif left_right_dist == 1 and front_dist == 0:
	loc_descr += "Just to the right of you"

	elif left_right_dist == -1 and front_dist == 0:
	loc_descr += "Just to the left of you"

	else:
	front_str = str(front_dist)+" steps in front of you " if front_dist > 0 else ""

	loc_descr += front_str

	suff = "s" if abs(left_right_dist) > 0 else ""
	and_ = "and" if loc_descr != "" else ""

	if left_right_dist < 0:
	left_right_str = f"{and_} {-left_right_dist} step{suff} to the left"
	loc_descr += left_right_str

	elif left_right_dist > 0:
	left_right_str = f"{and_} {left_right_dist} step{suff} to the right"
	loc_descr += left_right_str

	else:
	left_right_str = ""
	loc_descr += left_right_str

	loc_descr += f" there is a "

	obj_type = IDX_TO_OBJECT[object[0]]
	if obj_type == "npc":
	IDX_TO_STATE = {0: 'friendly', 1: 'antagonistic'}

	description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} peer. "

	# gaze
	gaze_dir = {
	0: "towards you",
	1: "to the left of you",
	2: "in the same direction as you",
	3: "to the right of you",
	}
	description += f"It is looking {gaze_dir[object[3]]}. "

	# point
	point_dir = {
	0: "towards you",
	1: "to the left of you",
	2: "in the same direction as you",
	3: "to the right of you",
	}

	if object[4] != 255:
	description += f"It is pointing {point_dir[object[4]]}. "

	# last action
	last_action = {v: k for k, v in SocialAINPCActionsDict.items()}[object[5]]


	last_action = {
	"go_forward": "foward",
	"rotate_left": "turn left",
	"rotate_right": "turn right",
	"toggle_action": "toggle",
	"point_stop_point": "stop pointing",
	"point_E": "",
	"point_S": "",
	"point_W": "",
	"point_N": "",
	"stop_point": "stop pointing",
	"no_op": ""
	}[last_action]

	if last_action not in ["no_op", ""]:
	description += f"It's last action is {last_action}. "

	elif obj_type in ["switch", "apple", "generatorplatform", "marble", "marbletee", "fence"]:

	if obj_type == "switch":
	# assumes that Switch.no_light == True
	assert object[-1] == 0

	description = f"{IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} "
	assert object[2:].mean() == 0

	elif obj_type == "lockablebox":
	IDX_TO_STATE = {0: 'open', 1: 'closed', 2: 'locked'}
	description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} "
	assert object[3:].mean() == 0

	elif obj_type == "applegenerator":
	IDX_TO_STATE = {1: 'square', 2: 'round'}
	description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} "
	assert object[3:].mean() == 0

	elif obj_type == "remotedoor":
	IDX_TO_STATE = {0: 'open', 1: 'closed'}
	description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} "
	assert object[3:].mean() == 0

	elif obj_type == "door":
	IDX_TO_STATE = {0: 'open', 1: 'closed', 2: 'locked'}
	description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} "
	assert object[3:].mean() == 0

	elif obj_type == "lever":
	IDX_TO_STATE = {1: 'activated', 0: 'unactivated'}
	if object[3] == 255:
	countdown_txt = ""
	else:
	countdown_txt = f"with {object[3]} timesteps left. "

	description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} {countdown_txt}"

	assert object[4:].mean() == 0
	else:
	raise ValueError(f"Undefined object type {obj_type}")

	full_destr = loc_descr + description + "\n"

	list_textual_descriptions.append(full_destr)

	if len(list_textual_descriptions) == 0:
	list_textual_descriptions.append("\n")

	return {'descriptions': list_textual_descriptions}

	class MultiModalMiniGridEnv(MiniGridEnv):

	grammar = None

	def reset(self, args, *kwargs):
	obs = super().reset()
	self.append_existing_utterance_to_history()
	obs = self.add_utterance_to_observation(obs)
	self.reset_utterance()
	return obs

	def append_existing_utterance_to_history(self):
	if self.utterance != self.empty_symbol:
	if self.utterance.startswith(self.empty_symbol):
	self.utterance_history += self.utterance[len(self.empty_symbol):]
	else:
	assert self.utterance == self.beginning_string
	self.utterance_history += self.utterance

	def add_utterance_to_observation(self, obs):
	obs["utterance"] = self.utterance
	obs["utterance_history"] = self.utterance_history
	obs["mission"] = "Hidden"
	return obs

	def reset_utterance(self):
	# set utterance to empty indicator
	self.utterance = self.empty_symbol

	def render(self, args, show_dialogue=True, *kwargs):

	obs = super().render(args, *kwargs)

	if args and args[0] == 'human':
	# draw text to the side of the image
	self.window.clear_text() # erase previous text
	if show_dialogue:
	self.window.set_caption(self.full_conversation)

	# self.window.ax.set_title("correct color: {}".format(self.box.target_color), loc="left", fontsize=10)

	if self.outcome_info:
	color = None
	if "SUCCESS" in self.outcome_info:
	color = "lime"
	elif "FAILURE" in self.outcome_info:
	color = "red"
	self.window.add_text(*(0.01, 0.85, self.outcome_info),
	**{'fontsize': 15, 'color': color, 'weight': "bold"})

	self.window.show_img(obs) # re-draw image to add changes to window
	return obs

	def add_obstacles(self):
	self.obstacles = self.parameters.get("Obstacles", "No") if self.parameters else "No"

	if self.obstacles != "No":
	n_stumps_range = {
	"A_bit": (1, 2),
	"Medium": (3, 4),
	"A_lot": (5, 6),
	}[self.obstacles]

	n_stumps = random.randint(*n_stumps_range)

	for _ in range(n_stumps):
	self.wall_start_x = self._rand_int(1, self.current_width - 2)
	self.wall_start_y = self._rand_int(1, self.current_height - 2)
	if random.choice([True, False]):
	self.grid.horz_wall(
	x=self.wall_start_x,
	y=self.wall_start_y,
	length=1
	)
	else:
	self.grid.horz_wall(
	x=self.wall_start_x,
	y=self.wall_start_y,
	length=1
	)