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bot.py
949 lines (768 loc) · 37.6 KB
/
bot.py
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from gym_minigrid.minigrid import *
from babyai.levels.verifier import *
from babyai.levels.verifier import (ObjDesc, pos_next_to,
GoToInstr, OpenInstr, PickupInstr, PutNextInstr, BeforeInstr, AndInstr, AfterInstr)
class DisappearedBoxError(Exception):
"""
Error that's thrown when a box is opened.
We make the assumption that the bot cannot accomplish the mission when it happens.
"""
def __init__(self, value):
self.value = value
def __str__(self):
return repr(self.value)
def manhattan_distance(pos, target):
return np.abs(target[0] - pos[0]) + np.abs(target[1] - pos[1])
class Subgoal:
"""The base class for all possible Bot subgoals.
Parameters:
----------
bot : Bot
The bot whose subgoal this is.
datum : object
The first parameter of the subgoal, e.g. a location or an object description.
reason : str
Why this subgoal was created. Subgoals created for different reasons require
similar but different behaviour.
"""
def __init__(self, bot=None, datum=None, reason=None):
self.bot = bot
self.datum = datum
self.reason = reason
self.update_agent_attributes()
self.actions = self.bot.mission.actions
def __repr__(self):
"""Mainly for debugging purposes"""
representation = '('
representation += type(self).__name__
if self.datum is not None:
representation += ': {}'.format(self.datum)
if self.reason is not None:
representation += ', reason: {}'.format(self.reason)
representation += ')'
return representation
def update_agent_attributes(self):
"""Should be called at each step before the replanning methods."""
self.pos = self.bot.mission.agent_pos
self.dir_vec = self.bot.mission.dir_vec
self.right_vec = self.bot.mission.right_vec
self.fwd_pos = self.pos + self.dir_vec
self.fwd_cell = self.bot.mission.grid.get(*self.fwd_pos)
self.carrying = self.bot.mission.carrying
def replan_before_action(self):
"""Change the plan if needed and return a suggested action.
This method is called at every iteration for the top-most subgoal
from the stack. It is supposed to return a suggested action if
it is clear how to proceed towards achieving the current subgoal.
If the subgoal is already achieved, or if it is not clear how it
can be achieved, or if is clear that a better plan exists,
this method can replan by pushing new subgoals
from the stack or popping the top one.
Returns:
-------
action : object
A suggection action if known, `None` the stack has been altered
and further replanning is required.
"""
raise NotImplementedError()
def replan_after_action(self, action_taken):
"""Change the plan when the taken action is known.
The action actually taken by the agent can be different from the one
suggested by `replan_before_action` is the bot can be used in
advising mode. This method is supposed to adjust the plan in the view
of the actual action taken.
"""
pass
def is_exploratory(self):
"""Whether the subgoal is exploratory or not.
Exploratory subgoals can be removed from the stack by the bot, e.g.
when no more exploration is required.
"""
return False
def _plan_undo_action(self, action_taken):
"""Plan how to undo the taken action."""
if action_taken == self.actions.forward:
# check if the 'forward' action was succesful
if not np.array_equal(self.bot.prev_agent_pos, self.pos):
self.bot.stack.append(GoNextToSubgoal(self.bot, self.pos))
elif action_taken == self.actions.left:
old_fwd_pos = self.pos + self.right_vec
self.bot.stack.append(GoNextToSubgoal(self.bot, self.pos + self.right_vec))
elif action_taken == self.actions.right:
old_fwd_pos = self.pos - self.right_vec
self.bot.stack.append(GoNextToSubgoal(self.bot, self.pos - self.right_vec))
elif action_taken == self.actions.drop and self.bot.prev_carrying != self.carrying:
# get that thing back, if dropping was succesful
assert self.fwd_cell.type in ('key', 'box', 'ball')
self.bot.stack.append(PickupSubgoal(self.bot))
elif action_taken == self.actions.pickup and self.bot.prev_carrying != self.carrying:
# drop that thing where you found it
fwd_cell = self.bot.mission.grid.get(*self.fwd_pos)
self.bot.stack.append(DropSubgoal(self.bot))
elif action_taken == self.actions.toggle:
# if you opened or closed a door, bring it back in the original state
fwd_cell = self.bot.mission.grid.get(*self.fwd_pos)
if (fwd_cell and fwd_cell.type == 'door'
and self.bot.fwd_door_was_open != fwd_cell.is_open):
self.bot.stack.append(CloseSubgoal(self.bot)
if fwd_cell.is_open
else OpenSubgoal(self.bot))
class CloseSubgoal(Subgoal):
def replan_before_action(self):
assert self.fwd_cell is not None, 'Forward cell is empty'
assert self.fwd_cell.type == 'door', 'Forward cell has to be a door'
assert self.fwd_cell.is_open, 'Forward door must be open'
return self.actions.toggle
def replan_after_action(self, action_taken):
if action_taken is None or action_taken == self.actions.toggle:
self.bot.stack.pop()
elif action_taken in [self.actions.forward, self.actions.left, self.actions.right]:
self._plan_undo_action(action_taken)
class OpenSubgoal(Subgoal):
"""Subgoal for opening doors.
Parameters:
----------
reason : str
`None`, `"Unlock"`, or `"UnlockAndKeepKey"`. If the reason is `"Unlock"`,
the agent will plan dropping the key somewhere after it opens the door
(see `replan_after_action`). When the agent faces the door, and the
reason is `None`, this subgoals replaces itself with a similar one,
but with with the reason `"Unlock"`. `reason="UnlockAndKeepKey` means
that the agent should not schedule the dropping of the key
when it faces a locked door, and should instead keep the key.
"""
def replan_before_action(self):
assert self.fwd_cell is not None, 'Forward cell is empty'
assert self.fwd_cell.type == 'door', 'Forward cell has to be a door'
# If the door is locked, go find the key and then return
# TODO: do we really need to be in front of the locked door
# to realize that we need the key for it ?
got_the_key = (self.carrying and self.carrying.type == 'key'
and self.carrying.color == self.fwd_cell.color)
if (self.fwd_cell.is_locked and not got_the_key):
# Find the key
key_desc = ObjDesc('key', self.fwd_cell.color)
key_desc.find_matching_objs(self.bot.mission)
# If we're already carrying something
if self.carrying:
self.bot.stack.pop()
# Find a location to drop what we're already carrying
drop_pos_cur = self.bot._find_drop_pos()
# Take back the object being carried
self.bot.stack.append(PickupSubgoal(self.bot))
self.bot.stack.append(GoNextToSubgoal(self.bot, drop_pos_cur))
# Go back to the door and open it
self.bot.stack.append(OpenSubgoal(self.bot))
self.bot.stack.append(GoNextToSubgoal(self.bot, tuple(self.fwd_pos)))
# Go to the key and pick it up
self.bot.stack.append(PickupSubgoal(self.bot))
self.bot.stack.append(GoNextToSubgoal(self.bot, key_desc))
# Drop the object being carried
self.bot.stack.append(DropSubgoal(self.bot))
self.bot.stack.append(GoNextToSubgoal(self.bot, drop_pos_cur))
else:
# This branch is will be used very rarely, given that
# GoNextToSubGoal(..., reason='Open') should plan
# going to the key before we get to stand right in front of a door.
# But the agent can be spawned right in front of a open door,
# for which we case we do need this code.
self.bot.stack.pop()
# Go back to the door and open it
self.bot.stack.append(OpenSubgoal(self.bot))
self.bot.stack.append(GoNextToSubgoal(self.bot, tuple(self.fwd_pos)))
# Go to the key and pick it up
self.bot.stack.append(PickupSubgoal(self.bot))
self.bot.stack.append(GoNextToSubgoal(self.bot, key_desc))
return
if self.fwd_cell.is_open:
self.bot.stack.append(CloseSubgoal(self.bot))
return
if self.fwd_cell.is_locked and self.reason is None:
self.bot.stack.pop()
self.bot.stack.append(OpenSubgoal(self.bot, reason='Unlock'))
return
return self.actions.toggle
def replan_after_action(self, action_taken):
if action_taken is None or action_taken == self.actions.toggle:
self.bot.stack.pop()
if self.reason == 'Unlock':
# The reason why this has to be planned after the action is taken
# is because if the position for dropping is chosen in advance,
# then by the time the key is dropped there, it might already
# be occupied.
drop_key_pos = self.bot._find_drop_pos()
self.bot.stack.append(DropSubgoal(self.bot))
self.bot.stack.append(GoNextToSubgoal(self.bot, drop_key_pos))
else:
self._plan_undo_action(action_taken)
class DropSubgoal(Subgoal):
def replan_before_action(self):
assert self.bot.mission.carrying
assert not self.fwd_cell
return self.actions.drop
def replan_after_action(self, action_taken):
if action_taken is None or action_taken == self.actions.drop:
self.bot.stack.pop()
elif action_taken in [self.actions.forward, self.actions.left, self.actions.right]:
self._plan_undo_action(action_taken)
class PickupSubgoal(Subgoal):
def replan_before_action(self):
assert not self.bot.mission.carrying
return self.actions.pickup
def replan_after_action(self, action_taken):
if action_taken is None or action_taken == self.actions.pickup:
self.bot.stack.pop()
elif action_taken in [self.actions.left, self.actions.right]:
self._plan_undo_action(action_taken)
class GoNextToSubgoal(Subgoal):
"""The subgoal for going next to objects or positions.
Parameters:
----------
datum : (int, int) tuple or `ObjDesc` or object reference
The position or the decription of the object or
the object to which we are going.
reason : str
One of the following:
- `None`: go the position (object) and face it
- `"PutNext"`: go face an empty position next to the object specified by `datum`
- `"Explore"`: going to a position, just like when the reason is `None`. The only
difference is that with this reason the subgoal will be considered
exploratory
"""
def replan_before_action(self):
target_obj = None
if isinstance(self.datum, ObjDesc):
target_obj, target_pos = self.bot._find_obj_pos(self.datum, self.reason == 'PutNext')
if not target_pos:
# No path found -> Explore the world
self.bot.stack.append(ExploreSubgoal(self.bot))
return
elif isinstance(self.datum, WorldObj):
target_obj = self.datum
target_pos = target_obj.cur_pos
else:
target_pos = tuple(self.datum)
# Suppore we are walking towards the door that we would like to open,
# it is locked, and we don't have the key. What do we do? If we are carrying
# something, it makes to just continue, as we still need to bring this object
# close to the door. If we are not carrying anything though, then it makes
# sense to change the plan and go straight for the required key.
if (self.reason == 'Open'
and target_obj and target_obj.type == 'door' and target_obj.is_locked):
key_desc = ObjDesc('key', target_obj.color)
key_desc.find_matching_objs(self.bot.mission)
if not self.carrying:
# No we need to commit to going to this particular door
self.bot.stack.pop()
self.bot.stack.append(GoNextToSubgoal(self.bot, target_obj, reason='Open'))
self.bot.stack.append(PickupSubgoal(self.bot))
self.bot.stack.append(GoNextToSubgoal(self.bot, key_desc))
return
# The position we are on is the one we should go next to
# -> Move away from it
if manhattan_distance(target_pos, self.pos) == (1 if self.reason == 'PutNext' else 0):
def steppable(cell):
return cell is None or (cell.type == 'door' and cell.is_open)
if steppable(self.fwd_cell):
return self.actions.forward
if steppable(self.bot.mission.grid.get(*(self.pos + self.right_vec))):
return self.actions.right
if steppable(self.bot.mission.grid.get(*(self.pos - self.right_vec))):
return self.actions.left
# Spin and hope for the best
return self.actions.left
# We are facing the target cell
# -> subgoal completed
if self.reason == 'PutNext':
if manhattan_distance(target_pos, self.fwd_pos) == 1:
if self.fwd_cell is None:
self.bot.stack.pop()
return
if self.fwd_cell.type == 'door' and self.fwd_cell.is_open:
# We can't drop an object in the cell where the door is.
# Instead, we add a subgoal on the stack that will force
# the bot to move the target object.
self.bot.stack.append(GoNextToSubgoal(
self.bot, self.fwd_pos + 2 * self.dir_vec))
return
else:
if np.array_equal(target_pos, self.fwd_pos):
self.bot.stack.pop()
return
# We are still far from the target
# -> try to find a non-blocker path
path, _, _ = self.bot._shortest_path(
lambda pos, cell: pos == target_pos,
)
# No non-blocker path found and
# reexploration within the room is not allowed or there is nothing to explore
# -> Look for blocker paths
if not path:
path, _, _ = self.bot._shortest_path(
lambda pos, cell: pos == target_pos,
try_with_blockers=True
)
# No path found
# -> explore the world
if not path:
self.bot.stack.append(ExploreSubgoal(self.bot))
return
# So there is a path (blocker, or non-blockers)
# -> try following it
next_cell = path[0]
# Choose the action in the case when the forward cell
# is the one we should go next to
if np.array_equal(next_cell, self.fwd_pos):
if self.fwd_cell:
if self.fwd_cell.type == 'door':
assert not self.fwd_cell.is_locked
if not self.fwd_cell.is_open:
self.bot.stack.append(OpenSubgoal(self.bot))
return
else:
return self.actions.forward
if self.carrying:
drop_pos_cur = self.bot._find_drop_pos()
drop_pos_block = self.bot._find_drop_pos(drop_pos_cur)
# Take back the object being carried
self.bot.stack.append(PickupSubgoal(self.bot))
self.bot.stack.append(GoNextToSubgoal(self.bot, drop_pos_cur))
# Pick up the blocking object and drop it
self.bot.stack.append(DropSubgoal(self.bot))
self.bot.stack.append(GoNextToSubgoal(self.bot, drop_pos_block))
self.bot.stack.append(PickupSubgoal(self.bot))
self.bot.stack.append(GoNextToSubgoal(self.bot, self.fwd_pos))
# Drop the object being carried
self.bot.stack.append(DropSubgoal(self.bot))
self.bot.stack.append(GoNextToSubgoal(self.bot, drop_pos_cur))
return
else:
drop_pos = self.bot._find_drop_pos()
self.bot.stack.append(DropSubgoal(self.bot))
self.bot.stack.append(GoNextToSubgoal(self.bot, drop_pos))
self.bot.stack.append(PickupSubgoal(self.bot))
return
else:
return self.actions.forward
# The forward cell is not the one we should go to
# -> turn towards the direction we need to go
if np.array_equal(next_cell - self.pos, self.right_vec):
return self.actions.right
elif np.array_equal(next_cell - self.pos, -self.right_vec):
return self.actions.left
# If we reacher this point in the code, then the cell is behind us.
# Instead of choosing left or right randomly,
# let's do something that might be useful:
# Because when we're GoingNextTo for the purpose of exploring,
# things might change while on the way to the position we're going to, we should
# pick this right or left wisely.
# The simplest thing we should do is: pick the one
# that doesn't lead you to face a non empty cell.
# One better thing would be to go to the direction
# where the closest wall/door is the furthest
distance_right = self.bot._closest_wall_or_door_given_dir(self.pos, self.right_vec)
distance_left = self.bot._closest_wall_or_door_given_dir(self.pos, -self.right_vec)
if distance_left > distance_right:
return self.actions.left
return self.actions.right
def replan_after_action(self, action_taken):
if action_taken in [self.actions.pickup, self.actions.drop, self.actions.toggle]:
self._plan_undo_action(action_taken)
def is_exploratory(self):
return self.reason == 'Explore'
class ExploreSubgoal(Subgoal):
def replan_before_action(self):
# Find the closest unseen position
_, unseen_pos, with_blockers = self.bot._shortest_path(
lambda pos, cell: not self.bot.vis_mask[pos],
try_with_blockers=True
)
if unseen_pos:
self.bot.stack.append(GoNextToSubgoal(self.bot, unseen_pos, reason='Explore'))
return None
# Find the closest unlocked unopened door
def unopened_unlocked_door(pos, cell):
return cell and cell.type == 'door' and not cell.is_locked and not cell.is_open
# Find the closest unopened door
def unopened_door(pos, cell):
return cell and cell.type == 'door' and not cell.is_open
# Try to find an unlocked door first.
# We do this because otherwise, opening a locked door as
# a subgoal may try to open the same door for exploration,
# resulting in an infinite loop.
_, door_pos, _ = self.bot._shortest_path(
unopened_unlocked_door, try_with_blockers=True)
if not door_pos:
# Try to find a locker door if an unlocked one is not available.
_, door_pos, _ = self.bot._shortest_path(
unopened_door, try_with_blockers=True)
# Open the door
if door_pos:
door_obj = self.bot.mission.grid.get(*door_pos)
# If we are going to a locked door, there are two cases:
# - we already have the key, then we should not drop it
# - we don't have the key, in which case eventually we should drop it
got_the_key = (self.carrying
and self.carrying.type == 'key' and self.carrying.color == door_obj.color)
open_reason = 'KeepKey' if door_obj.is_locked and got_the_key else None
self.bot.stack.pop()
self.bot.stack.append(OpenSubgoal(self.bot, reason=open_reason))
self.bot.stack.append(GoNextToSubgoal(self.bot, door_obj, reason='Open'))
return
assert False, "0nothing left to explore"
def is_exploratory(self):
return True
class Bot:
"""A bot that can solve all BabyAI levels.
The bot maintains a plan, represented as a stack of the so-called
subgoals. The initial set of subgoals is generated from the instruction.
The subgoals are then executed one after another, unless a change of
plan is required (e.g. the location of the target object is not known
or there other objects in the way). In this case, the bot changes the plan.
The bot can also be used to advice a suboptimal agent, e.g. play the
role of an oracle in algorithms like DAGGER. It changes the plan based on
the actual action that the agent took.
The main method of the bot (and the only one you are supposed to use) is `replan`.
Parameters:
----------
mission : a freshly created BabyAI environment
"""
def __init__(self, mission):
# Mission to be solved
self.mission = mission
# Grid containing what has been mapped out
self.grid = Grid(mission.width, mission.height)
# Visibility mask. True for explored/seen, false for unexplored.
self.vis_mask = np.zeros(shape=(mission.width, mission.height), dtype=np.bool)
# Stack of tasks/subtasks to complete (tuples)
self.stack = []
# Process/parse the instructions
self._process_instr(mission.instrs)
# How many BFS searches this bot has performed
self.bfs_counter = 0
# How many steps were made in total in all BFS searches
# performed by this bot
self.bfs_step_counter = 0
def replan(self, action_taken=None):
"""Replan and suggest an action.
Call this method once per every iteration of the environment.
Parameters:
----------
action_taken
The last action that the agent took. Can be `None`,
in which case the bot assumes that the action it suggested
was taken (or that it is the first iteration).
Returns:
-------
suggested_action
The action that the bot suggests. Can be `done` if the
bot thinks that the mission has been accomplished.
"""
self._process_obs()
# Check that no box has been opened
self._check_erroneous_box_opening(action_taken)
# TODO: instead of updating all subgoals, just add a couple
# properties to the `Subgoal` class.
for subgoal in self.stack:
subgoal.update_agent_attributes()
if self.stack:
self.stack[-1].replan_after_action(action_taken)
# Clear the stack from the non-essential subgoals
while self.stack and self.stack[-1].is_exploratory():
self.stack.pop()
suggested_action = None
while self.stack:
subgoal = self.stack[-1]
suggested_action = subgoal.replan_before_action()
# If is not clear what can be done for the current subgoal
# (because it is completed, because there is blocker,
# or because exploration is required), keep replanning
if suggested_action is not None:
break
if not self.stack:
suggested_action = self.mission.actions.done
self._remember_current_state()
return suggested_action
def _find_obj_pos(self, obj_desc, adjacent=False):
"""Find the position of the closest visible object matching a given description."""
assert len(obj_desc.obj_set) > 0
best_distance_to_obj = 999
best_pos = None
best_obj = None
for i in range(len(obj_desc.obj_set)):
try:
if obj_desc.obj_set[i] == self.mission.carrying:
continue
obj_pos = obj_desc.obj_poss[i]
if self.vis_mask[obj_pos]:
shortest_path_to_obj, _, with_blockers = self._shortest_path(
lambda pos, cell: pos == obj_pos,
try_with_blockers=True
)
assert shortest_path_to_obj is not None
distance_to_obj = len(shortest_path_to_obj)
if with_blockers:
# The distance should take into account the steps necessary
# to unblock the way. Instead of computing it exactly,
# we can use a lower bound on this number of steps
# which is 4 when the agent is not holding anything
# (pick, turn, drop, turn back
# and 7 if the agent is carrying something
# (turn, drop, turn back, pick,
# turn to other direction, drop, turn back)
distance_to_obj = (len(shortest_path_to_obj)
+ (7 if self.mission.carrying else 4))
# If we looking for a door and we are currently in that cell
# that contains the door, it will take us at least 2
# (3 if `adjacent == True`) steps to reach the goal.`
if distance_to_obj == 0:
distance_to_obj = 3 if adjacent else 2
# If what we want is to face a location that is adjacent to an object,
# and if we are already right next to this object,
# then we should not prefer this object to those at distance 2
if adjacent and distance_to_obj == 1:
distance_to_obj = 3
if distance_to_obj < best_distance_to_obj:
best_distance_to_obj = distance_to_obj
best_pos = obj_pos
best_obj = obj_desc.obj_set[i]
except IndexError:
# Suppose we are tracking red keys, and we just used a red key to open a door,
# then for the last i, accessing obj_desc.obj_poss[i] will raise an IndexError
# -> Solution: Not care about that red key we used to open the door
pass
return best_obj, best_pos
def _process_obs(self):
"""Parse the contents of an observation/image and update our state."""
grid, vis_mask = self.mission.gen_obs_grid()
view_size = self.mission.agent_view_size
pos = self.mission.agent_pos
f_vec = self.mission.dir_vec
r_vec = self.mission.right_vec
# Compute the absolute coordinates of the top-left corner
# of the agent's view area
top_left = pos + f_vec * (view_size - 1) - r_vec * (view_size // 2)
# Mark everything in front of us as visible
for vis_j in range(0, view_size):
for vis_i in range(0, view_size):
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.vis_mask.shape[0]:
continue
if abs_j < 0 or abs_j >= self.vis_mask.shape[1]:
continue
self.vis_mask[abs_i, abs_j] = True
def _remember_current_state(self):
self.prev_agent_pos = self.mission.agent_pos
self.prev_carrying = self.mission.carrying
fwd_cell = self.mission.grid.get(*self.mission.agent_pos + self.mission.dir_vec)
if fwd_cell and fwd_cell.type == 'door':
self.fwd_door_was_open = fwd_cell.is_open
self.prev_fwd_cell = fwd_cell
def _closest_wall_or_door_given_dir(self, position, direction):
distance = 1
while True:
position_to_try = position + distance * direction
# If the current position is outside the field of view,
# stop everything and return the previous one
if not self.mission.in_view(*position_to_try):
return distance - 1
cell = self.mission.grid.get(*position_to_try)
if cell and (cell.type.endswith('door') or cell.type == 'wall'):
return distance
distance += 1
def _breadth_first_search(self, initial_states, accept_fn, ignore_blockers):
"""Performs breadth first search.
This is pretty much your textbook BFS. The state space is agent's locations,
but the current direction is also added to the queue to slightly prioritize
going straight over turning.
"""
self.bfs_counter += 1
queue = [(state, None) for state in initial_states]
grid = self.mission.grid
previous_pos = dict()
while len(queue) > 0:
state, prev_pos = queue[0]
queue = queue[1:]
i, j, di, dj = state
if (i, j) in previous_pos:
continue
self.bfs_step_counter += 1
cell = grid.get(i, j)
previous_pos[(i, j)] = prev_pos
# If we reached a position satisfying the acceptance condition
if accept_fn((i, j), cell):
path = []
pos = (i, j)
while pos:
path.append(pos)
pos = previous_pos[pos]
return path, (i, j), previous_pos
# If this cell was not visually observed, don't expand from it
if not self.vis_mask[i, j]:
continue
if cell:
if cell.type == 'wall':
continue
# If this is a door
elif cell.type == 'door':
# If the door is closed, don't visit neighbors
if not cell.is_open:
continue
elif not ignore_blockers:
continue
# Location to which the bot can get without turning
# are put in the queue first
for k, l in [(di, dj), (dj, di), (-dj, -di), (-di, -dj)]:
next_pos = (i + k, j + l)
next_dir_vec = (k, l)
next_state = (*next_pos, *next_dir_vec)
queue.append((next_state, (i, j)))
# Path not found
return None, None, previous_pos
def _shortest_path(self, accept_fn, try_with_blockers=False):
"""
Finds the path to any of the locations that satisfy `accept_fn`.
Prefers the paths that avoid blockers for as long as possible.
"""
# Initial states to visit (BFS)
initial_states = [(*self.mission.agent_pos, *self.mission.dir_vec)]
path = finish = None
with_blockers = False
path, finish, previous_pos = self._breadth_first_search(
initial_states, accept_fn, ignore_blockers=False)
if not path and try_with_blockers:
with_blockers = True
path, finish, _ = self._breadth_first_search(
[(i, j, 1, 0) for i, j in previous_pos],
accept_fn, ignore_blockers=True)
if path:
# `path` now contains the path to a cell that is reachable without
# blockers. Now let's add the path to this cell
pos = path[-1]
extra_path = []
while pos:
extra_path.append(pos)
pos = previous_pos[pos]
path = path + extra_path[1:]
if path:
# And the starting position is not required
path = path[::-1]
path = path[1:]
# Note, that with_blockers only makes sense if path is not None
return path, finish, with_blockers
def _find_drop_pos(self, except_pos=None):
"""
Find a position where an object can be dropped, ideally without blocking anything.
"""
grid = self.mission.grid
def match_unblock(pos, cell):
# Consider the region of 8 neighboring cells around the candidate cell.
# If dropping the object in the candidate makes this region disconnected,
# then probably it is better to drop elsewhere.
i, j = pos
agent_pos = tuple(self.mission.agent_pos)
if np.array_equal(pos, agent_pos):
return False
if except_pos and np.array_equal(pos, except_pos):
return False
if not self.vis_mask[i, j] or grid.get(i, j):
return False
# We distinguish cells of three classes:
# class 0: the empty ones, including open doors
# class 1: those that are not interesting (just walls so far)
# class 2: all the rest, including objects and cells that are current not visible,
# and hence may contain objects, and also `except_pos` at it may soon contain
# an object
# We want to ensure that empty cells are connected, and that one can reach
# any object cell from any other object cell.
cell_class = []
for k, l in [(-1, -1), (0, -1), (1, -1), (1, 0),
(1, 1), (0, 1), (-1, 1), (-1, 0)]:
nb_pos = (i + k, j + l)
cell = grid.get(*nb_pos)
# compeletely blocked
if self.vis_mask[nb_pos] and cell and cell.type == 'wall':
cell_class.append(1)
# empty
elif (self.vis_mask[nb_pos]
and (not cell or (cell.type == 'door' and cell.is_open) or nb_pos == agent_pos)
and nb_pos != except_pos):
cell_class.append(0)
# an object cell
else:
cell_class.append(2)
# Now we need to check that empty cells are connected. To do that,
# let's check how many times empty changes to non-empty
changes = 0
for i in range(8):
if bool(cell_class[(i + 1) % 8]) != bool(cell_class[i]):
changes += 1
# Lastly, we need check that every object has an adjacent empty cell
for i in range(8):
next_i = (i + 1) % 8
prev_i = (i + 7) % 8
if cell_class[i] == 2 and cell_class[prev_i] != 0 and cell_class[next_i] != 0:
return False
return changes <= 2
def match_empty(pos, cell):
i, j = pos
if np.array_equal(pos, self.mission.agent_pos):
return False
if except_pos and np.array_equal(pos, except_pos):
return False
if not self.vis_mask[pos] or grid.get(*pos):
return False
return True
_, drop_pos, _ = self._shortest_path(match_unblock)
if not drop_pos:
_, drop_pos, _ = self._shortest_path(match_empty)
if not drop_pos:
_, drop_pos, _ = self._shortest_path(match_unblock, try_with_blockers=True)
if not drop_pos:
_, drop_pos, _ = self._shortest_path(match_empty, try_with_blockers=True)
return drop_pos
def _process_instr(self, instr):
"""
Translate instructions into an internal form the agent can execute
"""
if isinstance(instr, GoToInstr):
self.stack.append(GoNextToSubgoal(self, instr.desc))
return
if isinstance(instr, OpenInstr):
self.stack.append(OpenSubgoal(self))
self.stack.append(GoNextToSubgoal(self, instr.desc, reason='Open'))
return
if isinstance(instr, PickupInstr):
# We pick up and immediately drop so
# that we may carry other objects
self.stack.append(DropSubgoal(self))
self.stack.append(PickupSubgoal(self))
self.stack.append(GoNextToSubgoal(self, instr.desc))
return
if isinstance(instr, PutNextInstr):
self.stack.append(DropSubgoal(self))
self.stack.append(GoNextToSubgoal(self, instr.desc_fixed, reason='PutNext'))
self.stack.append(PickupSubgoal(self))
self.stack.append(GoNextToSubgoal(self, instr.desc_move))
return
if isinstance(instr, BeforeInstr) or isinstance(instr, AndInstr):
self._process_instr(instr.instr_b)
self._process_instr(instr.instr_a)
return
if isinstance(instr, AfterInstr):
self._process_instr(instr.instr_a)
self._process_instr(instr.instr_b)
return
assert False, "unknown instruction type"
def _check_erroneous_box_opening(self, action):
"""
When the agent opens a box, we raise an error and mark the task unsolvable.
This is a tad conservative, because maybe the box is irrelevant to the mission.
"""
if (action == self.mission.actions.toggle
and self.prev_fwd_cell is not None
and self.prev_fwd_cell.type == 'box'):
raise DisappearedBoxError('A box was opened. I am not sure I can help now.')