def __init__(self,
encoding_strategy,
num_actors,
episode_length,
percentage,
training=False,
weight_file_name='ppo_fitness_shaping_ranked',
learning_rate=1e-5,
discount_factor=0.99,
variance_bias_factor=0.98,
num_hidden_layers=1,
num_neurons=128,
batch_size=32,
clipping_value=0.2,
num_training_epochs=4,
dim_elimination_max_pooling=False,
entropy_factor=0.1,
entropy_factor_decay=0.05,
min_entropy_factor=0.01,
value_loss_factor=0.5,
prefer_higher_fitness=True):
"""
:param percentage: The percentage of fittest individuals to be selected from the population
:param prefer_higher_fitness: Whether a higher or lower fitness
is preferred during selection
"""
self.percentage = percentage
self.prefer_higher_fitness = prefer_higher_fitness
network = CombinedActorCriticNetwork(
encoding_strategy.num_channels(),
2,
eliminate_length_dimension=True,
eliminate_population_dimension=False,
dim_elimination_max_pooling=dim_elimination_max_pooling,
num_hidden_layers=num_hidden_layers,
num_neurons=num_neurons).cuda()
super().__init__(network,
encoding_strategy,
weight_file_name,
training=training,
learning_rate=learning_rate,
num_actors=num_actors,
episode_length=episode_length,
discount_factor=discount_factor,
variance_bias_factor=variance_bias_factor,
batch_size=batch_size,
clipping_value=clipping_value,
num_training_epochs=num_training_epochs,
finite_environment=True,
entropy_factor=entropy_factor,
entropy_factor_decay=entropy_factor_decay,
min_entropy_factor=min_entropy_factor,
value_loss_factor=value_loss_factor)
def __init__(
self,
encoding_strategy,
num_actors,
episode_length,
num_pairs,
training=False,
weight_file_name='ppo_fitness_shaping_tournament',
learning_rate=1e-5,
discount_factor=0.99,
variance_bias_factor=0.98,
num_hidden_layers=1,
num_neurons=128,
batch_size=32,
clipping_value=0.2,
num_training_epochs=4,
dim_elimination_max_pooling=False,
entropy_factor=0.1,
entropy_factor_decay=0.05,
min_entropy_factor=0.01,
value_loss_factor=0.5,
):
"""
:param num_pairs: Number of parent pairs that have to be generated
"""
self.tournament_strategy = TournamentParentPairingStrategy(num_pairs)
self.num_pairs = num_pairs
network = CombinedActorCriticNetwork(
encoding_strategy.num_channels(),
2,
eliminate_length_dimension=True,
eliminate_population_dimension=False,
dim_elimination_max_pooling=dim_elimination_max_pooling,
num_hidden_layers=num_hidden_layers,
num_neurons=num_neurons).cuda()
super().__init__(network,
encoding_strategy,
weight_file_name,
training=training,
learning_rate=learning_rate,
num_actors=num_actors,
episode_length=episode_length,
discount_factor=discount_factor,
variance_bias_factor=variance_bias_factor,
batch_size=batch_size,
clipping_value=clipping_value,
num_training_epochs=num_training_epochs,
finite_environment=True,
entropy_factor=entropy_factor,
entropy_factor_decay=entropy_factor_decay,
min_entropy_factor=min_entropy_factor,
value_loss_factor=value_loss_factor)
def __init__(
self,
encoding_strategy,
num_actors,
episode_length,
training=False,
weight_file_name='ppo_mutate_onestep_individual_learning_rates',
learning_rate=1e-5,
discount_factor=0.99,
variance_bias_factor=0.98,
num_hidden_layers=1,
num_neurons=128,
batch_size=32,
clipping_value=0.2,
num_training_epochs=4,
dim_elimination_max_pooling=False,
entropy_factor=0.1,
entropy_factor_decay=0.05,
min_entropy_factor=0.01,
value_loss_factor=0.5,
minimum_step_size=1e-5):
"""
:param minimum_step_size: The lower limit for step sizes
"""
num_output_channels = 2
self.minimum_step_size = minimum_step_size
network = CombinedActorCriticNetwork(
encoding_strategy.num_channels(),
num_output_channels,
eliminate_length_dimension=True,
dim_elimination_max_pooling=dim_elimination_max_pooling,
num_hidden_layers=num_hidden_layers,
num_neurons=num_neurons).cuda()
super().__init__(network,
encoding_strategy,
weight_file_name,
training=training,
learning_rate=learning_rate,
num_actors=num_actors,
episode_length=episode_length,
discount_factor=discount_factor,
variance_bias_factor=variance_bias_factor,
batch_size=batch_size,
clipping_value=clipping_value,
num_training_epochs=num_training_epochs,
finite_environment=True,
entropy_factor=entropy_factor,
entropy_factor_decay=entropy_factor_decay,
min_entropy_factor=min_entropy_factor,
value_loss_factor=value_loss_factor)
def __init__(self,
encoding_strategy,
num_actors,
episode_length,
training=False,
weight_file_name='ppo_population_subset',
learning_rate=1e-3,
min_subset_size=0,
discount_factor=0.99,
variance_bias_factor=0.98,
num_hidden_layers=1,
num_neurons=128,
batch_size=32,
clipping_value=0.2,
num_training_epochs=4,
dim_elimination_max_pooling=False,
entropy_factor=0.1,
entropy_factor_decay=0.05,
min_entropy_factor=0.01,
value_loss_factor=0.5):
"""
:param min_subset_size: The minimum number of individuals that has to be selected.
If too few are selected, min_subset_size random individuals are returned
"""
self.min_subset_size = min_subset_size
num_output_channels = 1
network = CombinedActorCriticNetwork(
encoding_strategy.num_channels(),
num_output_channels,
eliminate_length_dimension=True,
dim_elimination_max_pooling=dim_elimination_max_pooling,
num_hidden_layers=num_hidden_layers,
num_neurons=num_neurons).cuda()
super().__init__(network,
encoding_strategy,
weight_file_name,
training=training,
learning_rate=learning_rate,
num_actors=num_actors,
episode_length=episode_length,
discount_factor=discount_factor,
variance_bias_factor=variance_bias_factor,
batch_size=batch_size,
clipping_value=clipping_value,
num_training_epochs=num_training_epochs,
entropy_factor=entropy_factor,
entropy_factor_decay=entropy_factor_decay,
min_entropy_factor=min_entropy_factor,
value_loss_factor=value_loss_factor)
def __init__(self,
encoding_strategy,
population_size,
num_actors,
episode_length,
training=False,
weight_file_name='ppo_survivor_selection',
learning_rate=1e-5,
discount_factor=0.99,
variance_bias_factor=0.98,
num_hidden_layers=1,
num_neurons=128,
batch_size=32,
clipping_value=0.2,
num_training_epochs=4,
dim_elimination_max_pooling=False,
entropy_factor=0.1,
entropy_factor_decay=0.05,
min_entropy_factor=0.01,
value_loss_factor=0.5):
self.population_size = population_size
network = CombinedActorCriticNetwork(
encoding_strategy.num_channels(),
2,
eliminate_length_dimension=True,
dim_elimination_max_pooling=dim_elimination_max_pooling,
num_hidden_layers=num_hidden_layers,
num_neurons=num_neurons).cuda()
super().__init__(network,
encoding_strategy,
weight_file_name,
training=training,
learning_rate=learning_rate,
num_actors=num_actors,
episode_length=episode_length,
discount_factor=discount_factor,
variance_bias_factor=variance_bias_factor,
batch_size=batch_size,
clipping_value=clipping_value,
num_training_epochs=num_training_epochs,
finite_environment=True,
entropy_factor=entropy_factor,
entropy_factor_decay=entropy_factor_decay,
min_entropy_factor=min_entropy_factor,
value_loss_factor=value_loss_factor)
def __init__(
self,
encoding_strategy,
genome_creation_strategy,
num_actors,
episode_length,
training=False,
weight_file_name='ppo_individual_crossover_operator_selection',
learning_rate=1e-5,
discount_factor=0.99,
variance_bias_factor=0.98,
num_hidden_layers=1,
num_neurons=128,
batch_size=32,
clipping_value=0.2,
num_training_epochs=4,
dim_elimination_max_pooling=False,
entropy_factor=0.1,
entropy_factor_decay=0.05,
min_entropy_factor=0.01,
value_loss_factor=0.5,
crossover_rate=0.9,
):
"""
:param crossover_rate: The probability, with which crossover is performed, instead of copying the fittest parent
"""
self.operators = [
OnePointCrossoverStrategy(crossover_rate,
genome_creation_strategy),
TwoPointCrossoverStrategy(crossover_rate,
genome_creation_strategy),
LinearCrossoverStrategy(crossover_rate, genome_creation_strategy),
CyclicCrossoverStrategy(crossover_rate, genome_creation_strategy),
PositionCrossoverStrategy(crossover_rate,
genome_creation_strategy),
OrderCrossoverStrategy(crossover_rate, genome_creation_strategy),
PartiallyMappedCrossoverStrategy(crossover_rate,
genome_creation_strategy)
]
network = CombinedActorCriticNetwork(
2 * encoding_strategy.num_channels(),
len(self.operators),
eliminate_length_dimension=True,
eliminate_population_dimension=False,
dim_elimination_max_pooling=dim_elimination_max_pooling,
num_hidden_layers=num_hidden_layers,
num_neurons=num_neurons).cuda()
super().__init__(network,
encoding_strategy,
weight_file_name,
training=training,
learning_rate=learning_rate,
num_actors=num_actors,
episode_length=episode_length,
discount_factor=discount_factor,
variance_bias_factor=variance_bias_factor,
batch_size=batch_size,
clipping_value=clipping_value,
num_training_epochs=num_training_epochs,
finite_environment=True,
entropy_factor=entropy_factor,
entropy_factor_decay=entropy_factor_decay,
min_entropy_factor=min_entropy_factor,
value_loss_factor=value_loss_factor)