Spaces:
Sleeping
Sleeping
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): | |
super().__init__(color) | |
self.name = name | |
self.npc_dir = 1 # NPC initially looks downward | |
self.npc_type = 0 | |
self.env = env | |
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] | |
selected_door_id = self.env._rand_elem([0, 1]) | |
self.selected_door_pos = [self.env.door_pos_top, self.env.door_pos_bottom][selected_door_id] | |
self.selected_door = [self.env.door_top, self.env.door_bottom][selected_door_id] | |
def step(self): | |
if all(self.front_pos == self.selected_door_pos): | |
# in front of door | |
if self.selected_door.is_open: | |
self.go_forward() | |
else: | |
if (self.cur_pos[0] == self.selected_door_pos[0]) or (self.cur_pos[1] == self.selected_door_pos[1]): | |
# is either in the correct row on in the correct column | |
next_wanted_position = self.selected_door_pos | |
else: | |
# choose the midpoint | |
for cand_x, cand_y in [ | |
(self.cur_pos[0], self.selected_door_pos[1]), | |
(self.selected_door_pos[0], self.cur_pos[1]) | |
]: | |
print("wX:", self.env.wall_x) | |
print("wY:", self.env.wall_y) | |
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) | |
print("wanted_pos:", next_wanted_position) | |
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.") | |
class TwoDoorsIntentGrammar(object): | |
templates = ["Move your", "Shake your"] | |
things = ["body", "head"] | |
grammar_action_space = spaces.MultiDiscrete([len(templates), len(things)]) | |
def construct_utterance(cls, action): | |
return cls.templates[int(action[0])] + " " + cls.things[int(action[1])] + " " | |
class TwoDoorsIntentEnv(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), | |
*TwoDoorsIntentGrammar.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 top | |
door_color_top = self._rand_elem(COLOR_NAMES) | |
self.door_pos_top = (width-1, 1) | |
self.door_top = Door(door_color_top) | |
self.grid.set(*self.door_pos_top, self.door_top) | |
# switch top | |
self.switch_pos_top = (0, 1) | |
self.switch_top = Switch(door_color_top, lockable_object=self.door_top) | |
self.grid.set(*self.switch_pos_top, self.switch_top) | |
# door bottom | |
door_color_bottom = self._rand_elem(COLOR_NAMES) | |
self.door_pos_bottom = (width-1, height-2) | |
self.door_bottom = Door(door_color_bottom) | |
self.grid.set(*self.door_pos_bottom, self.door_bottom) | |
# switch bottom | |
self.switch_pos_bottom = (0, height-2) | |
self.switch_bottom = Switch(door_color_bottom, lockable_object=self.door_bottom) | |
self.grid.set(*self.switch_pos_bottom, self.switch_bottom) | |
# Set a randomly coloured Dancer NPC | |
color = self._rand_elem(COLOR_NAMES) | |
self.peer = Peer(color, "Jill", self) | |
# 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) | |
self.peer.step() | |
if np.isnan(p_action): | |
pass | |
if p_action == self.actions.done: | |
done = True | |
elif all(self.agent_pos == self.door_pos_top): | |
done = True | |
elif all(self.agent_pos == self.door_pos_bottom): | |
done = True | |
elif all([self.switch_top.is_on, self.switch_bottom.is_on]): | |
# if both switches are on no reward is given and episode ends | |
done = True | |
elif all(self.peer.cur_pos == self.peer.selected_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 TwoDoorsIntent8x8Env(TwoDoorsIntentEnv): | |
def __init__(self): | |
super().__init__(size=8) | |
class TwoDoorsIntent6x6Env(TwoDoorsIntentEnv): | |
def __init__(self): | |
super().__init__(size=6) | |
register( | |
id='MiniGrid-TwoDoorsIntent-5x5-v0', | |
entry_point='gym_minigrid.envs:TwoDoorsIntentEnv' | |
) | |
register( | |
id='MiniGrid-TwoDoorsIntent-6x6-v0', | |
entry_point='gym_minigrid.envs:TwoDoorsIntent6x6Env' | |
) | |
register( | |
id='MiniGrid-TwoDoorsIntent-8x8-v0', | |
entry_point='gym_minigrid.envs:TwoDoorsIntent8x8Env' | |
) | |