def init_evolution_algo(self):
"""Agent's GE algorithm operation defination."""
# Genetic algorithm parameters
self.operation_threshold = 50
self.genome_storage = []
# Grammatical Evolution part
from ponyge.algorithm.parameters import Parameters
parameter = Parameters()
parameter_list = ['--parameters', '../..,nm.txt']
# Comment when different results is desired.
# Else set this for testing purpose
# parameter.params['RANDOM_SEED'] = name
# # np.random.randint(1, 99999999)
# Set GE runtime parameters
parameter.params['POPULATION_SIZE'] = self.operation_threshold // 2
parameter.set_params(parameter_list)
self.parameter = parameter
# Initialize the genome
individual = initialisation(self.parameter, 1)
individual = evaluate_fitness(individual, self.parameter)
# Assign the genome to the agent
self.individual = individual
# Fitness
self.beta = 0.9
self.diversity_fitness = self.individual[0].fitness
self.individual[0].fitness = 0
self.generation = 0
def __init__(self, name, model):
"""Initialize the agent."""
super().__init__(name, model)
self.location = ()
self.direction = model.random.rand() * (2 * np.pi)
self.speed = 2
self.radius = 3
self.results = "db" # This can take 2 values. db or file
# self.exchange_time = model.random.randint(2, 4)
# This doesn't help. Maybe only perform genetic operations when
# an agents meet 10% of its total population
# """
self.operation_threshold = 2
self.genome_storage = []
# Define a BTContruct object
self.bt = BTConstruct(None, self)
# self.blackboard = Blackboard()
# self.blackboard.shared_content = dict()
self.shared_content = dict()
# self.shared_content = dict(
self.carryable = False
self.beta = 0.0001
self.food_collected = 0
# Grammatical Evolution part
from ponyge.algorithm.parameters import Parameters
parameter = Parameters()
parameter_list = ['--parameters', '../..,' + model.parm]
# Comment when different results is desired.
# Else set this for testing purpose
# parameter.params['RANDOM_SEED'] = name
# # np.random.randint(1, 99999999)
parameter.params['POPULATION_SIZE'] = self.operation_threshold // 2
parameter.set_params(parameter_list)
self.parameter = parameter
individual = initialisation(self.parameter, 1)
individual = evaluate_fitness(individual, self.parameter)
self.individual = individual
self.bt.xmlstring = self.individual[0].phenotype
self.bt.construct()
self.diversity_fitness = self.individual[0].fitness
self.delayed_reward = 0
# Location history
self.location_history = set()
self.timestamp = 0
self.step_count = 0
self.fitness_name = True
def __init__(self, name, model):
super().__init__(name, model)
self.location = ()
self.direction = model.random.rand() * (2 * np.pi)
self.speed = 2
self.radius = 3
# self.exchange_time = model.random.randint(2, 4)
# This doesn't help. Maybe only perform genetic operations when
# an agents meet 10% of its total population
# """
self.operation_threshold = 50
self.genome_storage = []
# Define a BTContruct object
# self.mapper = BTConstruct(None, None)
# Grammatical Evolution part
from ponyge.algorithm.parameters import Parameters
parameter = Parameters()
# list_params_files = ['string_match.txt', 'regression.txt', 'classification.txt']
# parameter_list = ['--parameters', 'string_match_dist.txt']
parameter_list = ['--parameters', '../,test_swarm.txt']
parameter.params['RANDOM_SEED'] = 1234 # np.random.randint(1, 99999999)
parameter.params['POPULATION_SIZE'] = self.operation_threshold // 2
parameter.set_params(parameter_list)
self.parameter = parameter
individual = initialisation(self.parameter, 1)
individual = evaluate_fitness(individual, self.parameter)
# self.mapper.xmlstring = self.individual.phenotype
self.individual = individual
if self.name == 4:
self.individual[0].fitness = 150
def __init__(self, name, model):
super().__init__(name, model)
self.location = ()
self.direction = model.random.rand() * (2 * np.pi)
self.speed = 2
self.radius = 3
# self.exchange_time = model.random.randint(2, 4)
# This doesn't help. Maybe only perform genetic operations when
# an agents meet 10% of its total population
# """
self.operation_threshold = 2
self.genome_storage = []
# Define a BTContruct object
self.bt = BTConstruct(None, self)
self.blackboard = Blackboard()
self.blackboard.shared_content = dict()
self.shared_content = dict()
# Grammatical Evolution part
from ponyge.algorithm.parameters import Parameters
parameter = Parameters()
parameter_list = ['--parameters', 'swarm.txt']
# Comment when different results is desired.
# Else set this for testing purpose
parameter.params['RANDOM_SEED'] = name
# np.random.randint(1, 99999999)
parameter.params['POPULATION_SIZE'] = self.operation_threshold // 2
parameter.set_params(parameter_list)
self.parameter = parameter
individual = initialisation(self.parameter, 1)
individual = evaluate_fitness(individual, self.parameter)
self.individual = individual
self.bt.xmlstring = self.individual[0].phenotype
self.bt.construct()
def __init__(self, name, model):
super().__init__(name, model)
self.location = ()
self.direction = model.random.rand() * (2 * np.pi)
self.speed = 2
self.radius = 3
self.operation_threshold = 2
self.genome_storage = []
# Define a BTContruct object
self.bt = BTConstruct(None, self)
self.blackboard = Blackboard()
self.blackboard.shared_content = dict()
# Grammatical Evolution part
from ponyge.algorithm.parameters import Parameters
parameter = Parameters()
parameter_list = ['--parameters', 'swarm.txt']
parameter.params['POPULATION_SIZE'] = self.operation_threshold // 2
parameter.params['RANDOM_SEED'] = model.seed
parameter.set_params(parameter_list)
self.parameter = parameter
individual = initialisation(self.parameter, 1)
self.individual = individual
self.bt.xmlstring = self.individual[0].phenotype
self.bt.construct()
self.output = py_trees.display.ascii_tree(self.bt.behaviour_tree.root)
# Location history
self.location_history = set()
self.timestamp = 0