def neat_bipedal_walker():
pop_size = 400
mutator = NEATMutator(new_edge_probability=0.1, new_node_probability=0.05)
genome = minimal(input_size=24, output_size=4, depth=5)
seeder = curry_genome_seeder(mutator=mutator, seed_genomes=[genome])
metric = generate_neat_metric(c_1=1, c_2=1, c_3=3)
population = NEATPopulation(population_size=pop_size,
delta=4,
genome_seeder=seeder,
metric=metric)
ds = DataStore(name='bip_walker_NEAT_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
population.speciate()
data = population.to_dict()
mutator(population)
ds.save(data)
print_progress(data)
return True
def neat_cart_pole():
pop_size = 1000
mutator = NEATMutator(new_edge_probability=0.1, new_node_probability=0.05)
seed_genome = minimal(input_size=4, output_size=1, depth=5)
seeder = curry_genome_seeder(mutator=mutator, seed_genomes=[seed_genome])
metric = generate_neat_metric(c_1=1, c_2=1, c_3=3)
population = NEATPopulation(population_size=pop_size,
genome_seeder=seeder,
delta=4,
metric=metric)
assign_population_fitness = build_env()
counter_fn = make_counter_fn()
for i in range(5):
success = assign_population_fitness(population)
if success and counter_fn():
break
population.speciate()
data = population.to_dict()
print_progress(data)
mutator(population)
score = run_env(data['best_genome'], env_name='CartPole-v0', render=True)
print(f'best_fitness: {score}')
return True
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 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'])