Spaces:
Running
Running
File size: 8,597 Bytes
be5548b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 |
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)])
@classmethod
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'
)
|