def begin_run(self):
"""Initializes run variables and writes a run header
to the log file.
This should be called before each run.
"""
self.eval_count = 0
self.local_best_score = -1
self.avg_score = 0
self.prev_avg_score = 0
self.stale_score_count_termination = 0
self.log.write_run_header(self.run_count)
# Initialize the population
self.population = []
for _ in range(self.population_size):
world = gpac_world_class.GPacWorld(self.config)
game_state = game_state_class.GameState(
world.pacman_coords, world.ghost_coords, world.pill_coords,
self.get_num_adj_walls(world, world.pacman_coords[0]))
pacman_cont = pacman_cont_class.PacmanController(self.config)
ghosts_cont = ghosts_cont_class.GhostsController(self.config)
game_state.update_walls(world.wall_coords)
self.population.append(
gpac_world_individual_class.GPacWorldIndividual(
world, game_state, pacman_cont, ghosts_cont))
def __init__(self, config):
"""Initializes the GPDriver class.
Where config is a Config object.
"""
self.config = config
self.seed = seed_class.Seed(self.config)
self.run_count = 1
self.eval_count = 1
self.local_best_score = -1
self.log = log_class.Log(self.config, self.seed, overwrite=True)
self.global_best_score = -1
self.gpac_world = gpac_world_class.GPacWorld(self.config,
initial_instance=True)
self.pacman_cont = pacman_cont_class.PacmanController()
self.ghosts_cont = ghosts_cont_class.GhostsController()
self.game_state = game_state_class.GameState(
self.gpac_world.pacman_coord, self.gpac_world.ghost_coords,
self.gpac_world.pill_coords)
def breed(parent_a, parent_b):
"""Performs sub-tree crossover on parent_a and parent_b returning the child tree."""
def crossover_recursive(receiver_index, donator_index):
if receiver_index < len(
child_pacman_cont.state_evaluator
) and donator_index < len(parent_pacman_cont.state_evaluator):
return
child_pacman_cont.state_evaluator[
receiver_index] = tree.TreeNode(
receiver_index, parent_pacman_cont.
state_evaluator[donator_index].value)
crossover_recursive(
child_pacman_cont.state_evaluator.get_left_child_index(
receiver_index),
parent_pacman_cont.state_evaluator.get_left_child_index(
donator_index))
crossover_recursive(
child_pacman_cont.state_evaluator.get_right_child_index(
receiver_index),
parent_pacman_cont.state_evaluator.get_right_child_index(
donator_index))
# Choose a random node (crossover point) from each state evaluator node list
crossover_node_a = parent_a.pacman_cont.state_evaluator[
random.choices([
n for n in parent_a.pacman_cont.state_evaluator if n.value
])[0].index]
crossover_node_b = parent_b.pacman_cont.state_evaluator[
random.choices([
n for n in parent_b.pacman_cont.state_evaluator if n.value
])[0].index]
child_pacman_cont = copy.copy(parent_a.pacman_cont)
parent_pacman_cont = parent_b.pacman_cont
# Extend the child's state evaluator if necessary
if len(child_pacman_cont.state_evaluator) < len(
parent_a.pacman_cont.state_evaluator):
child_pacman_cont.state_evaluator = child_pacman_cont.state_evaluator + [
tree.TreeNode(index, None) for index in range(
len(child_pacman_cont.state_evaluator),
len(parent_a.pacman_cont.state_evaluator))
]
# Perform sub-tree crossover
crossover_recursive(crossover_node_a.index, crossover_node_b.index)
# Finish generating the child
world = gpac_world_class.GPacWorld(self.config)
game_state = game_state_class.GameState(
world.pacman_coords, world.ghost_coords, world.pill_coords,
self.get_num_adj_walls(world, world.pacman_coords[0]))
pacman_cont = child_pacman_cont
ghosts_cont = parent_a.ghosts_cont
game_state.update_walls(world.wall_coords)
child = gpac_world_individual_class.GPacWorldIndividual(
world, game_state, pacman_cont, ghosts_cont)
return child
def begin_run(self):
"""Initializes run variables and writes a run header
to the log file.
This should be called before each run.
"""
self.eval_count = 0
self.local_best_pacman_fitness = -1
self.pacman_avg_fitness = 0
self.prev_pacman_avg_fitness = 0
self.stale_fitness_count_termination = 0
self.log.write_run_header(self.run_count)
# Initialize the populations
self.gpac_world_population = []
self.pacman_cont_population = []
self.ghost_cont_population = []
for _ in range(
max(self.pacman_population_size, self.ghost_population_size,
self.pacman_child_population_size,
self.ghost_child_population_size)):
world = gpac_world_class.GPacWorld(self.config)
game_state = game_state_class.GameState(
world.pacman_coords, world.ghost_coords, world.pill_coords, [
self.get_num_adj_walls(world, pacman_coord)
for pacman_coord in world.pacman_coords
])
game_state.update_walls(world.wall_coords)
self.gpac_world_population.append(
gpac_world_individual_class.GPacWorldIndividual(
world, game_state))
# BONUS2
if self.use_single_pacman_cont:
num_pacman_conts = 1
else:
num_pacman_conts = self.num_pacmen
for _ in range(self.pacman_population_size):
self.pacman_cont_population.append(
cont_class.ControllerIndividual([
pacman_cont_class.PacmanController(self.config)
for _ in range(num_pacman_conts)
]))
# BONUS1, BONUS2
if self.use_single_ghost_cont:
num_ghost_conts = 1
else:
num_ghost_conts = self.num_ghosts
for _ in range(self.ghost_population_size):
self.ghost_cont_population.append(
cont_class.ControllerIndividual([
ghost_cont_class.GhostController(self.config)
for _ in range(num_ghost_conts)
]))
def begin_eval(self):
"""(Re)initializes the GPacWorld class member variable.
This should be called prior to each evaluation.
"""
self.gpac_world = gpac_world_class.GPacWorld(self.config)