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import numpy as np
from gym_minigrid.minigrid import *
from gym_minigrid.register import register
import time
from collections import deque
class Peer(NPC):
"""
A dancing NPC that the agent has to copy
"""
def __init__(self, color, name, env, knowledgeable=False):
super().__init__(color)
self.name = name
self.npc_dir = 1 # NPC initially looks downward
self.npc_type = 0
self.env = env
self.knowledgeable = knowledgeable
self.npc_actions = []
self.dancing_step_idx = 0
self.actions = MiniGridEnv.Actions
self.add_npc_direction = True
self.available_moves = [self.rotate_left, self.rotate_right, self.go_forward, self.toggle_action]
self.exited = False
def step(self):
if self.exited:
return
if all(np.array(self.cur_pos) == np.array(self.env.door_pos)):
# todo: disappear
# todo: close door
self.env.grid.set(*self.cur_pos, self.env.object)
self.cur_pos = np.array([np.nan, np.nan])
self.env.object.toggle(self.env, self.cur_pos)
self.exited = True
elif self.knowledgeable:
if all(self.front_pos == self.env.door_pos):
# in front of door
if self.env.object.is_open:
self.go_forward()
else:
self.toggle_action()
else:
if (self.cur_pos[0] == self.env.door_pos[0]) or (self.cur_pos[1] == self.env.door_pos[1]):
# is either in the correct row on in the correct column
next_wanted_position = self.env.door_pos
else:
# choose the midpoint
for cand_x, cand_y in [
(self.cur_pos[0], self.env.door_pos[1]),
(self.env.door_pos[0], self.cur_pos[1])
]:
if (
cand_x > 0 and cand_x < self.env.wall_x
) and (
cand_y > 0 and cand_y < self.env.wall_y
):
next_wanted_position = (cand_x, cand_y)
if self.cur_pos[1] == next_wanted_position[1]:
# same y
if self.cur_pos[0] < next_wanted_position[0]:
wanted_dir = 0
else:
wanted_dir = 2
if self.npc_dir == wanted_dir:
self.go_forward()
else:
self.rotate_left()
elif self.cur_pos[0] == next_wanted_position[0]:
# same x
if self.cur_pos[1] < next_wanted_position[1]:
wanted_dir = 1
else:
wanted_dir = 3
if self.npc_dir == wanted_dir:
self.go_forward()
else:
self.rotate_left()
else:
raise ValueError("Something is wrong.")
else:
self.env._rand_elem(self.available_moves)()
class BoboGrammar(object):
templates = ["Move your", "Shake your"]
things = ["body", "head"]
grammar_action_space = spaces.MultiDiscrete([len(templates), len(things)])
@classmethod
def construct_utterance(cls, action):
return cls.templates[int(action[0])] + " " + cls.things[int(action[1])] + " "
class BoboEnv(MultiModalMiniGridEnv):
"""
Environment in which the agent is instructed to go to a given object
named using an English text string
"""
def __init__(
self,
size=5,
diminished_reward=True,
step_penalty=False,
knowledgeable=False,
):
assert size >= 5
self.empty_symbol = "NA \n"
self.diminished_reward = diminished_reward
self.step_penalty = step_penalty
self.knowledgeable = knowledgeable
super().__init__(
grid_size=size,
max_steps=5*size**2,
# Set this to True for maximum speed
see_through_walls=True,
actions=MiniGridEnv.Actions,
action_space=spaces.MultiDiscrete([
len(MiniGridEnv.Actions),
*BoboGrammar.grammar_action_space.nvec
]),
add_npc_direction=True
)
print({
"size": size,
"diminished_reward": diminished_reward,
"step_penalty": step_penalty,
})
def _gen_grid(self, width, height):
# Create the grid
self.grid = Grid(width, height, nb_obj_dims=4)
# Randomly vary the room width and height
width = self._rand_int(5, width+1)
height = self._rand_int(5, height+1)
self.wall_x = width - 1
self.wall_y = height - 1
# Generate the surrounding walls
self.grid.wall_rect(0, 0, width, height)
door_color = self._rand_elem(COLOR_NAMES)
wall_for_door = self._rand_int(0, 4)
if wall_for_door < 2:
w = self._rand_int(1, width-1)
h = height-1 if wall_for_door == 0 else 0
else:
w = width-1 if wall_for_door == 3 else 0
h = self._rand_int(1, height-1)
self.door_pos = (w, h)
self.door = Door(door_color)
self.grid.set(*self.door_pos, self.door)
# Set a randomly coloured Dancer NPC
color = self._rand_elem(COLOR_NAMES)
self.peer = Peer(color, "Jim", self, knowledgeable=self.knowledgeable)
# Place it on the middle left side of the room
peer_pos = np.array((self._rand_int(1, width - 1), self._rand_int(1, height - 1)))
self.grid.set(*peer_pos, self.peer)
self.peer.init_pos = peer_pos
self.peer.cur_pos = peer_pos
# Randomize the agent's start position and orientation
self.place_agent(size=(width, height))
# Generate the mission string
self.mission = 'watch dancer and repeat his moves afterwards'
# Dummy beginning string
self.beginning_string = "This is what you hear. \n"
self.utterance = self.beginning_string
# utterance appended at the end of each step
self.utterance_history = ""
# used for rendering
self.conversation = self.utterance
def step(self, action):
p_action = action[0]
utterance_action = action[1:]
obs, reward, done, info = super().step(p_action)
if np.isnan(p_action):
pass
if p_action == self.actions.done:
done = True
self.peer.step()
if all(self.agent_pos == self.door_pos):
reward = self._reward()
done = True
# discount
if self.step_penalty:
reward = reward - 0.01
# fill observation with text
self.append_existing_utterance_to_history()
obs = self.add_utterance_to_observation(obs)
self.reset_utterance()
return obs, reward, done, info
def _reward(self):
if self.diminished_reward:
return super()._reward()
else:
return 1.0
def render(self, *args, **kwargs):
obs = super().render(*args, **kwargs)
print("conversation:\n", self.conversation)
print("utterance_history:\n", self.utterance_history)
self.window.set_caption(self.conversation, [self.peer.name])
return obs
class Bobo8x8Env(BoboEnv):
def __init__(self):
super().__init__(size=8)
class Bobo6x6Env(BoboEnv):
def __init__(self):
super().__init__(size=6)
# knowledgeable
class BoboKnowledgeableEnv(BoboEnv):
def __init__(self):
super().__init__(size=5, knowledgeable=True)
class BoboKnowledgeable6x6Env(BoboEnv):
def __init__(self):
super().__init__(size=6, knowledgeable=True)
class BoboKnowledgeable8x8Env(BoboEnv):
def __init__(self):
super().__init__(size=8, knowledgeable=True)
register(
id='MiniGrid-Bobo-5x5-v0',
entry_point='gym_minigrid.envs:BoboEnv'
)
register(
id='MiniGrid-Bobo-6x6-v0',
entry_point='gym_minigrid.envs:Bobo6x6Env'
)
register(
id='MiniGrid-Bobo-8x8-v0',
entry_point='gym_minigrid.envs:Bobo8x8Env'
)
register(
id='MiniGrid-BoboKnowledgeable-5x5-v0',
entry_point='gym_minigrid.envs:BoboKnowledgeableEnv'
)
register(
id='MiniGrid-BoboKnowledgeable-6x6-v0',
entry_point='gym_minigrid.envs:BoboKnowledgeable6x6Env'
)
register(
id='MiniGrid-BoboKnowledgeable-8x8-v0',
entry_point='gym_minigrid.envs:BoboKnowledgeable8x8Env'
)
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