def cart_pole_res_example():
genome = dense(input_size=4, output_size=1, layer_dims=[2, 2, 2])
weights_len = len(genome.edges) + len(genome.nodes)
init_mu = np.random.uniform(-1, 1, weights_len)
mutator = ADRESMutator(initial_mu=init_mu, std_dev=0.1)
seeder = curry_genome_seeder(mutator=mutator, seed_genomes=[genome])
population = RESPopulation(population_size=50, genome_seeder=seeder)
assign_population_fitness = build_env()
counter_fn = make_counter_fn()
for i in range(100):
success = assign_population_fitness(population)
if success and counter_fn():
break
data = population.to_dict()
mutator(population)
print(
f'generation: {i}, mean score: {data["mean_fitness"]}, best score: {data["best_fitness"]}'
)
data = population.to_dict()
run_env(data['best_genome'], render=True, env_name='CartPole-v0')
def test_dense_factory(self):
from gerel.algorithms.RES.population import RESPopulation
from gerel.algorithms.RES.mutator import RESMutator
from gerel.populations.genome_seeders import curry_genome_seeder
genome = dense(
input_size=1,
output_size=1,
layer_dims=[4]
)
weights_len = len(genome.edges) + len(genome.nodes)
init_mu = np.random.uniform(-1, 1, weights_len)
mutator = RESMutator(
initial_mu=init_mu,
std_dev=0.1,
alpha=0.1
)
seeder = curry_genome_seeder(
mutator=mutator,
seed_genomes=[genome]
)
RESPopulation(
population_size=1,
genome_seeder=seeder
)
def bipedal_walker_RES():
genome = dense(
input_size=24,
output_size=4,
layer_dims=[20]
)
weights_len = len(genome.edges) + len(genome.nodes)
init_mu = np.random.uniform(-1, 1, weights_len)
mutator = RESMutator(
initial_mu=init_mu,
std_dev=0.5,
alpha=1
)
seeder = curry_genome_seeder(
mutator=mutator,
seed_genomes=[genome]
)
population = RESPopulation(
population_size=400,
genome_seeder=seeder
)
ds = DataStore(name='bip_walker_RES_data')
assign_population_fitness = build_env(
env_name='BipedalWalker-v3',
num_steps=200,
repetition=1)
counter_fn = make_counter_fn()
for i in range(500):
success = assign_population_fitness(population)
if success and counter_fn():
break
data = population.to_dict()
mutator(population)
print_progress(data)
ds.save(data)
return True
def cart_pole_simple_example():
genome = dense(input_size=4, output_size=1, layer_dims=[2, 2, 2])
mutator = SIMPLEMutator(std_dev=0.1, survival_rate=0.1)
seeder = curry_genome_seeder(mutator=mutator, seed_genomes=[genome])
population = SIMPLEPopulation(population_size=50, genome_seeder=seeder)
assign_population_fitness = build_env()
counter_fn = make_counter_fn()
for i in range(100):
success = assign_population_fitness(population)
if success and counter_fn():
break
data = population.to_dict()
mutator(population)
print(
f'generation: {i}, mean score: {data["mean_fitness"]}, best score: {data["best_fitness"]}'
)
run_env(data['best_genome'])