# -*- coding:utf-8 -*-
#
# Copyright (C) 2020-2021, Saarland University
# Copyright (C) 2020-2021, Maximilian Köhl <koehl@cs.uni-saarland.de>
# Copyright (C) 2020-2021, Michaela Klauck <klauck@cs.uni-saarland.de>
from __future__ import annotations
import dataclasses as d
import typing as t
import enum
import itertools
import math
import re
from momba import model
from momba.model import expressions, types
from momba.moml import expr, prop
[docs]class TankType(enum.Enum):
"""
An enumeration of different *tank types*.
The actual tank size is calculate based on the size of the
track and *capacity factor*.
Attributes
----------
capacity_factor:
The capacity factor associated with the tank size.
"""
SMALL = 0.5
""" A small tank. """
MEDIUM = 0.75
""" A medium-sized tank. """
LARGE = 1
""" A large tank. """
capacity_factor: float
def __init__(self, capacity_factor: float) -> None:
self.capacity_factor = capacity_factor
class AccelerationModel(t.Protocol):
def __call__(self, acceleration: model.Expression) -> model.Expression:
pass
[docs]class Underground(enum.Enum):
"""
An enumeration of different *undergrounds*.
Undergrounds introduce probabilistic noise modeling
slippery road conditions.
Attributes
----------
acceleration_probability:
An expression for the probability that the acceleration succeeds.
acceleration_model:
A function for computing the *abnormal* acceleration.
"""
TARMAC = expr("9 / 10"), lambda a: a
"""
A very solid non-slippery underground introducing no noise.
"""
SLIPPERY_TARMAC = expr("9 / 10"), lambda a: expr("0")
"""
A solid but somewhat slippery underground.
"""
SAND = (
expr("5 / 10"),
lambda a: expr("$a > 0 ? $a - 1 : ($a < 0 ? $a + 1 : 0)", a=a),
)
"""
A sandy underground introducing some noise, be cautious!
"""
ICE = expr("3 / 10"), lambda a: expr("0")
"""
A very slippy underground.
"""
acceleration_probability: model.Expression
acceleration_model: AccelerationModel
def __init__(
self,
acceleration_probability: model.Expression,
acceleration_model: AccelerationModel,
) -> None:
self.acceleration_probability = acceleration_probability
self.acceleration_model = acceleration_model
[docs]@d.dataclass(frozen=True, order=True)
class Coordinate:
"""
Represents a coordinate on the track.
"""
x: int
""" The :math:`x` coordinate. """
y: int
""" The :math:`y` coordinate. """
[docs]class CellType(enum.Enum):
"""
An enumeration of *cell types*.
"""
BLANK = ".", 0
"""
A *blank cell* where one can drive.
"""
BLOCKED = "x", 1
"""
A cell *blocked* by an obstacle.
"""
START = "s", 2
"""
A start cell.
"""
GOAL = "g", 3
"""
A goal cell.
"""
symbol: str
number: int
def __init__(self, symbol: str, number: int) -> None:
self.symbol = symbol
self.number = number
class Direction(enum.Enum):
NORTH = (0, -1)
NORTH_EAST = (1, -1)
EAST = (1, 0)
SOUTH_EAST = (1, 1)
SOUTH = (0, 1)
SOUTH_WEST = (-1, 1)
WEST = (-1, 0)
NORTH_WEST = (-1, -1)
delta: Coordinate
def __init__(self, delta_x: int, delta_y: int) -> None:
self.delta = Coordinate(delta_x, delta_y)
@property
def distance_variable(self) -> str:
return f"dist_{self.name.lower()}"
[docs]@d.dataclass(frozen=True)
class Track:
"""
Represents a *track*.
Attributes
----------
width:
The width of the track.
height:
The height of the track.
blank_cells:
The set of blank cells.
blocked_cells:
The set of blocked cells.
start_cells:
The set of start cells.
goal_cells:
The set of goal cells.
"""
width: int
height: int
blank_cells: t.FrozenSet[Coordinate]
blocked_cells: t.FrozenSet[Coordinate]
start_cells: t.FrozenSet[Coordinate]
goal_cells: t.FrozenSet[Coordinate]
[docs] def get_cell_type(self, cell: Coordinate) -> CellType:
"""
Retrives the type of the given *cell*.
"""
if cell in self.blank_cells:
return CellType.BLANK
elif cell in self.blocked_cells:
return CellType.BLOCKED
elif cell in self.start_cells:
return CellType.START
else:
assert cell in self.goal_cells
return CellType.GOAL
@property
def textual_description(self) -> str:
"""
Converts the track into its textual description.
"""
lines = [f"dim: {self.width} {self.height}"]
for y in range(self.height):
lines.append(
"".join(
self.get_cell_type(Coordinate(x, y)).value
for x in range(self.width)
)
)
return "\n".join(lines)
[docs] @classmethod
def from_source(cls, source: str) -> Track:
"""
Converts a textual specification of a track into a :class:`Track`.
"""
firstline, _, remainder = source.partition("\n")
dimension = re.match(r"dim: (?P<height>\d+) (?P<width>\d+)", firstline)
assert dimension is not None, "invalid format: dimension missing"
width, height = int(dimension["width"]), int(dimension["height"])
track = [
list(line.strip())
for line in remainder.splitlines(keepends=False)
if line.strip()
]
assert (
len(track) == height
), "given track height does not match actual track height"
assert all(
len(row) == width for row in track
), "given track width does not match actual track width"
def get_coordinates(expected_cell_char: str) -> t.FrozenSet[Coordinate]:
return frozenset(
Coordinate(x, y)
for y, row in enumerate(track)
for x, cell_char in enumerate(row)
if cell_char == expected_cell_char
)
blank_cells = get_coordinates(".")
blocked_cells = get_coordinates("x")
start_cells = get_coordinates("s")
goal_cells = get_coordinates("g")
assert len(start_cells) > 0, "no start cell specified"
assert len(goal_cells) > 0, "no goal cell specified"
return cls(width, height, blank_cells, blocked_cells, start_cells, goal_cells)
class FuelModel(t.Protocol):
def __call__(
self, scenario: Scenario, dx: model.Expression, dy: model.Expression
) -> model.Expression:
pass
def fuel_model_linear(
scenario: Scenario, dx: model.Expression, dy: model.Expression
) -> model.Expression:
return expr("abs($dx) + abs($dy)", dx=dx, dy=dy)
def fuel_model_quadratic(
scenario: Scenario, dx: model.Expression, dy: model.Expression
) -> model.Expression:
return expr("$linear ** 2", linear=fuel_model_linear(scenario, dx, dy))
def fuel_model_regular(
scenario: Scenario, dx: model.Expression, dy: model.Expression
) -> model.Expression:
return expr(
"(1 + $max_acceleration) + $quadratic",
max_acceleration=scenario.max_acceleration,
quadratic=fuel_model_quadratic(scenario, dx, dy),
)
[docs]@d.dataclass(frozen=True)
class Scenario:
"""
A scenario description comprising a track, start cell, tank type, underground,
maximal speed and acceleration values, and a fuel model.
"""
track: Track
start_cell: t.Optional[Coordinate]
tank_type: TankType = TankType.LARGE
underground: Underground = Underground.TARMAC
max_speed: t.Optional[int] = None
max_acceleration: int = 1
fuel_model: t.Optional[FuelModel] = fuel_model_regular
compute_distances: bool = False
random_start: bool = False
@property
def tank_size(self) -> int:
return math.floor(
self.tank_type.capacity_factor * 3 * len(self.track.blank_cells)
)
@property
def possible_accelerations(self) -> t.Iterable[int]:
return tuple(range(-self.max_acceleration, self.max_acceleration + 1))
def compute_consumption(
self, dx: model.Expression, dy: model.Expression
) -> model.Expression:
assert self.fuel_model is not None, "no fuel model has been defined"
return self.fuel_model(self, dx, dy)
def construct_model(scenario: Scenario) -> model.Network:
"""
Constructs an MDP network from the provided scenario description.
"""
ctx = model.Context(model.ModelType.MDP)
network = ctx.create_network(name="Featured Racetrack")
track = scenario.track
ctx.global_scope.declare_constant("WIDTH", types.INT, value=track.width)
ctx.global_scope.declare_constant("HEIGHT", types.INT, value=track.height)
speed_bound = (
max(scenario.track.width, scenario.track.height) + scenario.max_acceleration
)
ctx.global_scope.declare_variable(
"car_dx",
types.INT.bound(-speed_bound, speed_bound),
initial_value=0,
)
ctx.global_scope.declare_variable(
"car_dy",
types.INT.bound(-speed_bound, speed_bound),
initial_value=0,
)
first_start_cell = next(iter(scenario.track.start_cells))
ctx.global_scope.declare_variable(
"car_x",
types.INT.bound(-1, track.width),
initial_value=scenario.start_cell.x
if scenario.start_cell is not None
else first_start_cell.x,
)
ctx.global_scope.declare_variable(
"car_y",
types.INT.bound(-1, track.height),
initial_value=scenario.start_cell.y
if scenario.start_cell is not None
else first_start_cell.y,
)
ctx.global_scope.declare_variable(
"map",
typ=types.array_of(types.array_of(types.INT.bound(0, 3))),
is_transient=True,
initial_value=model.expressions.ArrayValue(
tuple(
model.expressions.ArrayValue(
tuple(
model.ensure_expr(track.get_cell_type(Coordinate(x, y)).number)
for x in range(track.width)
)
)
for y in range(track.height)
)
),
)
if scenario.compute_distances:
ctx.global_scope.declare_variable(
"goal_dist_x",
typ=types.INT.bound(-track.width - 1, track.width + 1),
initial_value=0,
)
ctx.global_scope.declare_variable(
"goal_dist_y",
typ=types.INT.bound(-track.height - 1, track.height + 1),
initial_value=0,
)
ctx.global_scope.declare_variable(
"goal_dist",
typ=types.INT.bound(0, track.width + track.height + 2),
initial_value=0,
)
for direction in Direction:
ctx.global_scope.declare_variable(
direction.distance_variable,
typ=types.INT.bound(
0, math.floor(math.sqrt(track.width**2 + track.height**2)) + 1
),
initial_value=0,
)
if scenario.fuel_model is not None:
ctx.global_scope.declare_variable(
"fuel",
types.INT.bound(0, scenario.tank_size),
initial_value=scenario.tank_size,
)
accelerate = ctx.create_action_type("accelerate").create_pattern()
# The environment is about to move the car.
move_tick = ctx.create_action_type("move_tick").create_pattern()
# The environment is about to check the state of the car.
check_tick = ctx.create_action_type("check_tick").create_pattern()
# The environment is about to delegate the decision back to the car.
delegate = ctx.create_action_type("delegate").create_pattern()
def is_off_track(
car_x: model.Expression = expr("car_x"),
car_y: model.Expression = expr("car_y"),
):
return expr(
"$car_x >= WIDTH or $car_x < 0 or $car_y >= HEIGHT or $car_y < 0",
car_x=car_x,
car_y=car_y,
)
def is_at_cell(
typ: CellType,
car_x: model.Expression = expr("car_x"),
car_y: model.Expression = expr("car_y"),
):
return model.expressions.ite(
is_off_track(car_x, car_y),
model.ensure_expr(typ is CellType.BLOCKED),
expr(
f"$cell_number == {typ.number}",
cell_number=model.expressions.ArrayAccess(
model.expressions.ArrayAccess(expr("map"), car_y), car_x
),
),
)
def is_at_goal(
car_x: model.Expression = expr("car_x"),
car_y: model.Expression = expr("car_y"),
) -> model.Expression:
return is_at_cell(CellType.GOAL, car_x, car_y)
def is_at_blocked(
car_x: model.Expression = expr("car_x"),
car_y: model.Expression = expr("car_y"),
) -> model.Expression:
return is_at_cell(CellType.BLOCKED, car_x, car_y)
# In case the fuel is empty before reaching the goal, the model goes
# into a dead state without transitions. Hence, this property also
# covers the consumption of fuel.
ctx.define_property(
"goalProbability",
prop("min({ Pmax(F($is_at_goal)) | initial })", is_at_goal=is_at_goal()),
)
def construct_car_automaton() -> model.Automaton:
automaton = ctx.create_automaton(name="car")
initial = automaton.create_location(initial=True)
def compute_speed(
current: model.Expression, acceleration: expressions.ValueOrExpression
) -> model.Expression:
if scenario.max_speed is None:
return expr(
"$current + $acceleration",
current=current,
acceleration=acceleration,
)
else:
return expr(
"max(min($current + $acceleration, $max_speed), -$max_speed)",
current=current,
acceleration=acceleration,
max_speed=scenario.max_speed,
)
for ax, ay in itertools.product(scenario.possible_accelerations, repeat=2):
automaton.create_edge(
source=initial,
destinations=[
model.create_destination(
location=initial,
assignments={
"car_dx": compute_speed(expr("car_dx"), ax),
"car_dy": compute_speed(expr("car_dy"), ay),
},
probability=scenario.underground.acceleration_probability,
),
model.create_destination(
location=initial,
assignments={
"car_dx": compute_speed(
expr("car_dx"),
scenario.underground.acceleration_model(ax),
),
"car_dy": compute_speed(
expr("car_dy"),
scenario.underground.acceleration_model(ay),
),
},
probability=expr(
"1 - $p",
p=scenario.underground.acceleration_probability,
),
),
],
action_pattern=accelerate,
annotation={"ax": ax, "ay": ay},
)
return automaton
def construct_tank_automaton() -> model.Automaton:
automaton = ctx.create_automaton(name="tank")
initial = automaton.create_location(initial=True)
consumption = scenario.compute_consumption(expr("car_dx"), expr("car_dy"))
automaton.create_edge(
source=initial,
destinations=[
model.create_destination(
initial,
assignments={
"fuel": expr(
"fuel - floor($consumption)", consumption=consumption
)
},
)
],
action_pattern=check_tick,
guard=expr(
"fuel >= $consumption",
consumption=consumption,
),
)
return automaton
def construct_environment_automaton() -> model.Automaton:
automaton = ctx.create_automaton(name="environment")
automaton.scope.declare_variable(
"start_x", typ=types.INT.bound(-1, track.width), initial_value=0
)
automaton.scope.declare_variable(
"start_y", typ=types.INT.bound(-1, track.height), initial_value=0
)
automaton.scope.declare_variable(
"counter",
typ=types.INT.bound(0, max(track.width, track.height) + 1),
initial_value=0,
)
initial = automaton.create_location("initial", initial=True)
position_set = automaton.create_location("position_set")
wait_for_car = automaton.create_location("wait_for_car")
move_car = automaton.create_location("move_car")
env_check = automaton.create_location("env_check")
move_ticks = expr("max(abs(car_dx), abs(car_dy))")
if scenario.start_cell is None:
options = set(track.start_cells)
if scenario.random_start:
options.update(track.blank_cells)
automaton.create_edge(
initial,
destinations=[
model.create_destination(
position_set,
assignments={
"car_x": model.ensure_expr(start_cell.x),
"car_y": model.ensure_expr(start_cell.y),
},
probability=expr(f"1 / {len(options)}"),
)
for start_cell in options
],
)
else:
automaton.create_edge(
initial,
destinations=[
model.create_destination(
position_set,
assignments={
"car_x": model.ensure_expr(scenario.start_cell.x),
"car_y": model.ensure_expr(scenario.start_cell.y),
},
)
],
)
automaton.create_edge(
source=position_set,
destinations=[model.create_destination(wait_for_car)],
action_pattern=delegate,
)
# Wait for the decision of the car.
automaton.create_edge(
source=wait_for_car,
destinations=[
model.create_destination(
location=move_car,
assignments={
"counter": expr("0"),
"start_x": expr("car_x"),
"start_y": expr("car_y"),
},
)
],
action_pattern=accelerate,
)
# Move the car or delegate the decision back to the car.
automaton.create_edge(
source=move_car,
destinations=[
model.create_destination(
env_check,
assignments={
"counter": expr("counter + 1"),
"car_x": expr(
"start_x + floor((counter + 1) * (car_dx / $move_ticks) + 0.5)",
move_ticks=move_ticks,
),
"car_y": expr(
"start_y + floor((counter + 1) * (car_dy / $move_ticks) + 0.5)",
move_ticks=move_ticks,
),
},
)
],
guard=expr("counter < $move_ticks", move_ticks=move_ticks),
action_pattern=move_tick,
)
automaton.create_edge(
source=move_car,
destinations=[model.create_destination(wait_for_car)],
guard=expr("counter >= $move_ticks", move_ticks=move_ticks),
action_pattern=delegate,
)
# Checker whether we should terminate or continue moving the car.
should_terminate = expr(
"$is_off_track or $is_at_goal or $is_at_blocked",
is_off_track=is_off_track(),
is_at_goal=is_at_goal(),
is_at_blocked=is_at_blocked(),
)
automaton.create_edge(
source=env_check,
destinations=[model.create_destination(move_car)],
guard=expr("not $should_terminate", should_terminate=should_terminate),
action_pattern=check_tick,
)
return automaton
def construct_distance_automaton() -> model.Automaton:
automaton = ctx.create_automaton(name="Distance")
compute = automaton.create_location("compute")
done = automaton.create_location("done")
wait = automaton.create_location("wait", initial=True)
def get_goal_dist_x(goal: Coordinate) -> model.Expression:
return expr(f"{goal.x} - car_x")
def get_goal_dist_y(goal: Coordinate) -> model.Expression:
return expr(f"{goal.y} - car_y")
def get_goal_dist(goal: Coordinate) -> model.Expression:
return expr(
"abs($dist_x) + abs($dist_y)",
dist_x=get_goal_dist_x(goal),
dist_y=get_goal_dist_y(goal),
)
automaton.create_edge(
done,
destinations=[model.create_destination(wait)],
action_pattern=accelerate,
)
assignments = {
direction.distance_variable: model.ensure_expr(0) for direction in Direction
}
assignments["goal_dist"] = model.ensure_expr(track.width + track.height + 2)
for goal in track.goal_cells:
assignments["goal_dist"] = model.expressions.minimum(
get_goal_dist(goal), assignments["goal_dist"]
)
automaton.create_edge(
wait,
destinations=[
model.create_destination(
compute,
assignments=assignments,
)
],
action_pattern=delegate,
)
def get_current_x(direction: Direction):
return expr(
"car_x + $factor * $distance",
factor=direction.delta.x,
distance=expr(direction.distance_variable),
)
def get_current_y(direction: Direction):
return expr(
"car_y + $factor * $distance",
factor=direction.delta.y,
distance=expr(direction.distance_variable),
)
def is_done(direction: Direction):
return model.expressions.logic_or(
is_at_blocked(get_current_x(direction), get_current_y(direction)),
is_off_track(get_current_x(direction), get_current_y(direction)),
)
is_done_all = model.expressions.logic_all(
*(is_done(direction) for direction in Direction)
)
assignments = {"goal_dist_x": track.width + 1, "goal_dist_y": track.height + 1}
for goal in track.goal_cells:
do_apply = expr(
"goal_dist == $to_this_goal", to_this_goal=get_goal_dist(goal)
)
assignments["goal_dist_x"] = model.expressions.ite(
do_apply, get_goal_dist_x(goal), assignments["goal_dist_x"]
)
assignments["goal_dist_y"] = model.expressions.ite(
do_apply, get_goal_dist_y(goal), assignments["goal_dist_y"]
)
automaton.create_edge(
compute,
destinations=[model.create_destination(done, assignments=assignments)],
guard=is_done_all,
)
automaton.create_edge(
compute,
destinations=[
model.create_destination(
compute,
assignments={
direction.distance_variable: expr(
f"{direction.distance_variable} + $delta",
delta=model.expressions.ite(is_done(direction), 0, 1),
)
for direction in Direction
},
)
],
guard=expr("not $is_done", is_done=is_done_all),
)
return automaton
car = construct_car_automaton().create_instance()
environment = construct_environment_automaton().create_instance()
accelerate_vector = {car: accelerate, environment: accelerate}
check_tick_vector = {environment: check_tick}
delegate_vector = {environment: delegate}
if scenario.fuel_model:
tank = construct_tank_automaton().create_instance()
check_tick_vector[tank] = check_tick
if scenario.compute_distances:
instance = construct_distance_automaton().create_instance()
delegate_vector[instance] = delegate
accelerate_vector[instance] = accelerate
network.create_link(accelerate_vector, result=accelerate)
network.create_link({environment: move_tick}, result=move_tick)
network.create_link(check_tick_vector, result=check_tick)
network.create_link(delegate_vector, result=delegate)
return network
def generate_scenarios(
track: Track, speed_bound: int, acceleration_bound: int
) -> t.Iterator[Scenario]:
for start_cell in track.start_cells:
for max_speed in range(1, speed_bound + 1):
for max_acceleration in range(1, acceleration_bound + 1):
for underground in Underground:
for tank_type in TankType:
yield Scenario(
track,
start_cell,
tank_type,
underground,
max_speed,
max_acceleration,
compute_distances=True,
)
