def __init__(self, width, height, agent_type):
self.width = width
self.height = height
self.agent = BeerTrackerAgent(x=randrange(WORLD_WIDTH - AGENT_SIZE),
agent_type=agent_type)
self.beer_object_index = 0
self.pulling = False
self.nr_of_pulls = 0
if agent_type == 3:
self.beer_objects = [BeerObject() for _ in range(TIMESTEPS + 1)]
else:
self.beer_objects = [
BeerObject() for _ in range((TIMESTEPS // WORLD_HEIGHT) + 1)
]
self.beer_object = self.beer_objects[0]
# Two separate things should be measured, the ability to avoid big beers and the ability to catch the small ones
# The first score list is [taken, avoided, partly avoided] for small, the second is the same for big beers.
self.score = [[0, 0, 0], [0, 0, 0]]
self.pull_score = [[0, 0, 0], [0, 0, 0]]
# I need to keep the randomly spawned beer positions to be able to recreate a scenario.
self.initial_beer_objects = deepcopy(self.beer_objects)
self.initial_agent_x = self.agent.x
def __init__(self, width, height, agent_type):
self.width = width
self.height = height
self.agent = BeerTrackerAgent(x=randrange(WORLD_WIDTH - AGENT_SIZE), agent_type=agent_type)
self.beer_object_index = 0
self.pulling = False
self.nr_of_pulls = 0
if agent_type == 3:
self.beer_objects = [BeerObject() for _ in range(TIMESTEPS + 1)]
else:
self.beer_objects = [BeerObject() for _ in range((TIMESTEPS//WORLD_HEIGHT) + 1)]
self.beer_object = self.beer_objects[0]
# Two separate things should be measured, the ability to avoid big beers and the ability to catch the small ones
# The first score list is [taken, avoided, partly avoided] for small, the second is the same for big beers.
self.score = [[0, 0, 0], [0, 0, 0]]
self.pull_score = [[0, 0, 0], [0, 0, 0]]
# I need to keep the randomly spawned beer positions to be able to recreate a scenario.
self.initial_beer_objects = deepcopy(self.beer_objects)
self.initial_agent_x = self.agent.x
class BeerTrackerWorld:
def __init__(self, width, height, agent_type):
self.width = width
self.height = height
self.agent = BeerTrackerAgent(x=randrange(WORLD_WIDTH - AGENT_SIZE), agent_type=agent_type)
self.beer_object_index = 0
self.pulling = False
self.nr_of_pulls = 0
if agent_type == 3:
self.beer_objects = [BeerObject() for _ in range(TIMESTEPS + 1)]
else:
self.beer_objects = [BeerObject() for _ in range((TIMESTEPS//WORLD_HEIGHT) + 1)]
self.beer_object = self.beer_objects[0]
# Two separate things should be measured, the ability to avoid big beers and the ability to catch the small ones
# The first score list is [taken, avoided, partly avoided] for small, the second is the same for big beers.
self.score = [[0, 0, 0], [0, 0, 0]]
self.pull_score = [[0, 0, 0], [0, 0, 0]]
# I need to keep the randomly spawned beer positions to be able to recreate a scenario.
self.initial_beer_objects = deepcopy(self.beer_objects)
self.initial_agent_x = self.agent.x
def drop_object_one_level(self):
if self.beer_object.drop_one_level():
self.calculate_score_of_meal(self.score)
self.beer_object_index += 1
self.beer_object = self.beer_objects[self.beer_object_index]
def pull_object(self):
self.beer_object.pull()
self.calculate_score_of_meal(self.pull_score)
self.beer_object_index += 1
self.nr_of_pulls += 1
self.beer_object = self.beer_objects[self.beer_object_index]
def calculate_score_of_meal(self, score):
# Catch
if self.agent.x <= self.beer_object.x and \
self.agent.x + self.agent.size >= self.beer_object.x + self.beer_object.size:
if self.beer_object.size <= BEER_MAX_WANTED_SIZE:
score[0][0] += 1
else:
score[1][0] += 1
# Avoid
elif self.agent.x > self.beer_object.x + self.beer_object.size or \
self.agent.x + self.agent.size < self.beer_object.x:
if self.beer_object.size <= BEER_MAX_WANTED_SIZE:
score[0][1] += 1
else:
score[1][1] += 1
# Partly hit
else:
if self.beer_object.size <= BEER_MAX_WANTED_SIZE:
score[0][2] += 1
else:
score[1][2] += 1
def prediction_to_maneuver(self, prediction):
best_index = prediction.argmax()
self.pulling = False
# x_direction_size = int(floor(prediction[best_index] * (AGENT_MAX_NR_OF_STEPS + 1 - 0.0001)))
if ONE_HOT_OUTPUT:
if best_index == 0:
pass
elif best_index == 1:
self.agent.move_right(x_direction_size=1)
elif best_index == 2:
self.agent.move_right(x_direction_size=2)
elif best_index == 3:
self.agent.move_right(x_direction_size=3)
elif best_index == 4:
self.agent.move_right(x_direction_size=4)
elif best_index == 5:
self.agent.move_left(x_direction_size=1)
elif best_index == 6:
self.agent.move_left(x_direction_size=2)
elif best_index == 7:
self.agent.move_left(x_direction_size=3)
elif best_index == 8:
self.agent.move_left(x_direction_size=4)
elif self.agent.agent_type == 3 and best_index == 9:
self.pull_object()
self.pulling = True
else:
if prediction[0] > 0.5:
if self.agent.agent_type == 3 and prediction[2] > prediction[0]:
self.pull_object()
self.pulling = True
# Move right
elif prediction[1] < 0.2:
pass
elif prediction[1] < 0.4:
self.agent.move_right(x_direction_size=1)
elif prediction[1] < 0.6:
self.agent.move_right(x_direction_size=2)
elif prediction[1] < 0.8:
self.agent.move_right(x_direction_size=3)
else:
self.agent.move_right(x_direction_size=4)
else:
if self.agent.agent_type == 3 and prediction[2] < prediction[0]:
self.pull_object()
self.pulling = True
# Move left
elif prediction[1] < 0.2:
pass
elif prediction[1] < 0.4:
self.agent.move_left(x_direction_size=1)
elif prediction[1] < 0.6:
self.agent.move_left(x_direction_size=2)
elif prediction[1] < 0.8:
self.agent.move_left(x_direction_size=3)
else:
self.agent.move_left(x_direction_size=4)
def reset(self):
self.score = [[0, 0, 0], [0, 0, 0]]
self.pull_score = [[0, 0, 0], [0, 0, 0]]
self.beer_object_index = 0
self.nr_of_pulls = 0
self.beer_objects = deepcopy(self.initial_beer_objects)
self.beer_object = self.beer_objects[self.beer_object_index]
self.agent.x = self.initial_agent_x
self.beer_object.update_range()
self.agent.update_range()
class BeerTrackerWorld:
def __init__(self, width, height, agent_type):
self.width = width
self.height = height
self.agent = BeerTrackerAgent(x=randrange(WORLD_WIDTH - AGENT_SIZE),
agent_type=agent_type)
self.beer_object_index = 0
self.pulling = False
self.nr_of_pulls = 0
if agent_type == 3:
self.beer_objects = [BeerObject() for _ in range(TIMESTEPS + 1)]
else:
self.beer_objects = [
BeerObject() for _ in range((TIMESTEPS // WORLD_HEIGHT) + 1)
]
self.beer_object = self.beer_objects[0]
# Two separate things should be measured, the ability to avoid big beers and the ability to catch the small ones
# The first score list is [taken, avoided, partly avoided] for small, the second is the same for big beers.
self.score = [[0, 0, 0], [0, 0, 0]]
self.pull_score = [[0, 0, 0], [0, 0, 0]]
# I need to keep the randomly spawned beer positions to be able to recreate a scenario.
self.initial_beer_objects = deepcopy(self.beer_objects)
self.initial_agent_x = self.agent.x
def drop_object_one_level(self):
if self.beer_object.drop_one_level():
self.calculate_score_of_meal(self.score)
self.beer_object_index += 1
self.beer_object = self.beer_objects[self.beer_object_index]
def pull_object(self):
self.beer_object.pull()
self.calculate_score_of_meal(self.pull_score)
self.beer_object_index += 1
self.nr_of_pulls += 1
self.beer_object = self.beer_objects[self.beer_object_index]
def calculate_score_of_meal(self, score):
# Catch
if self.agent.x <= self.beer_object.x and \
self.agent.x + self.agent.size >= self.beer_object.x + self.beer_object.size:
if self.beer_object.size <= BEER_MAX_WANTED_SIZE:
score[0][0] += 1
else:
score[1][0] += 1
# Avoid
elif self.agent.x > self.beer_object.x + self.beer_object.size or \
self.agent.x + self.agent.size < self.beer_object.x:
if self.beer_object.size <= BEER_MAX_WANTED_SIZE:
score[0][1] += 1
else:
score[1][1] += 1
# Partly hit
else:
if self.beer_object.size <= BEER_MAX_WANTED_SIZE:
score[0][2] += 1
else:
score[1][2] += 1
def prediction_to_maneuver(self, prediction):
best_index = prediction.argmax()
self.pulling = False
# x_direction_size = int(floor(prediction[best_index] * (AGENT_MAX_NR_OF_STEPS + 1 - 0.0001)))
if ONE_HOT_OUTPUT:
if best_index == 0:
pass
elif best_index == 1:
self.agent.move_right(x_direction_size=1)
elif best_index == 2:
self.agent.move_right(x_direction_size=2)
elif best_index == 3:
self.agent.move_right(x_direction_size=3)
elif best_index == 4:
self.agent.move_right(x_direction_size=4)
elif best_index == 5:
self.agent.move_left(x_direction_size=1)
elif best_index == 6:
self.agent.move_left(x_direction_size=2)
elif best_index == 7:
self.agent.move_left(x_direction_size=3)
elif best_index == 8:
self.agent.move_left(x_direction_size=4)
elif self.agent.agent_type == 3 and best_index == 9:
self.pull_object()
self.pulling = True
else:
if prediction[0] > 0.5:
if self.agent.agent_type == 3 and prediction[2] > prediction[0]:
self.pull_object()
self.pulling = True
# Move right
elif prediction[1] < 0.2:
pass
elif prediction[1] < 0.4:
self.agent.move_right(x_direction_size=1)
elif prediction[1] < 0.6:
self.agent.move_right(x_direction_size=2)
elif prediction[1] < 0.8:
self.agent.move_right(x_direction_size=3)
else:
self.agent.move_right(x_direction_size=4)
else:
if self.agent.agent_type == 3 and prediction[2] < prediction[0]:
self.pull_object()
self.pulling = True
# Move left
elif prediction[1] < 0.2:
pass
elif prediction[1] < 0.4:
self.agent.move_left(x_direction_size=1)
elif prediction[1] < 0.6:
self.agent.move_left(x_direction_size=2)
elif prediction[1] < 0.8:
self.agent.move_left(x_direction_size=3)
else:
self.agent.move_left(x_direction_size=4)
def reset(self):
self.score = [[0, 0, 0], [0, 0, 0]]
self.pull_score = [[0, 0, 0], [0, 0, 0]]
self.beer_object_index = 0
self.nr_of_pulls = 0
self.beer_objects = deepcopy(self.initial_beer_objects)
self.beer_object = self.beer_objects[self.beer_object_index]
self.agent.x = self.initial_agent_x
self.beer_object.update_range()
self.agent.update_range()