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change repo structure; modify readme

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wls2002
2024-03-26 21:58:27 +08:00
parent 6970e6a6d5
commit 47dbcbea80
69 changed files with 74 additions and 60 deletions
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36
examples/brax/ant.py
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from pipeline import Pipeline
from algorithm.neat import *
from problem.rl_env import BraxEnv
from utils import Act
if __name__ == '__main__':
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
genome=DefaultGenome(
num_inputs=27,
num_outputs=8,
max_nodes=50,
max_conns=100,
node_gene=DefaultNodeGene(
activation_options=(Act.tanh,),
activation_default=Act.tanh,
)
),
pop_size=1000,
species_size=10,
),
),
problem=BraxEnv(
env_name='ant',
),
generation_limit=10000,
fitness_target=5000
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)

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36
examples/brax/half_cheetah.py
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from pipeline import Pipeline
from algorithm.neat import *
from problem.rl_env import BraxEnv
from utils import Act
if __name__ == '__main__':
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
genome=DefaultGenome(
num_inputs=17,
num_outputs=6,
max_nodes=50,
max_conns=100,
node_gene=DefaultNodeGene(
activation_options=(Act.tanh,),
activation_default=Act.tanh,
)
),
pop_size=1000,
species_size=10,
),
),
problem=BraxEnv(
env_name='halhcheetah',
),
generation_limit=10000,
fitness_target=5000
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)

36
examples/brax/reacher.py
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@@ -1,36 +0,0 @@
from pipeline import Pipeline
from algorithm.neat import *
from problem.rl_env import BraxEnv
from utils import Act
if __name__ == '__main__':
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
genome=DefaultGenome(
num_inputs=11,
num_outputs=2,
max_nodes=50,
max_conns=100,
node_gene=DefaultNodeGene(
activation_options=(Act.tanh,),
activation_default=Act.tanh,
)
),
pop_size=100,
species_size=10,
),
),
problem=BraxEnv(
env_name='reacher',
),
generation_limit=10000,
fitness_target=5000
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)

32
examples/func_fit/xor.py
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@@ -1,32 +0,0 @@
from pipeline import Pipeline
from algorithm.neat import *
from problem.func_fit import XOR3d
if __name__ == '__main__':
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
genome=DefaultGenome(
num_inputs=3,
num_outputs=1,
max_nodes=50,
max_conns=100,
),
pop_size=10000,
species_size=10,
compatibility_threshold=3.5,
),
),
problem=XOR3d(),
generation_limit=10000,
fitness_target=-1e-8
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)
# show result
pipeline.show(state, best)

51
examples/func_fit/xor3d_hyperneat.py
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@@ -1,51 +0,0 @@
from pipeline import Pipeline
from algorithm.neat import *
from algorithm.hyperneat import *
from utils import Act
from problem.func_fit import XOR3d
if __name__ == '__main__':
pipeline = Pipeline(
algorithm=HyperNEAT(
substrate=FullSubstrate(
input_coors=[(-1, -1), (0.333, -1), (-0.333, -1), (1, -1)],
hidden_coors=[
(-1, -0.5), (0.333, -0.5), (-0.333, -0.5), (1, -0.5),
(-1, 0), (0.333, 0), (-0.333, 0), (1, 0),
(-1, 0.5), (0.333, 0.5), (-0.333, 0.5), (1, 0.5),
],
output_coors=[(0, 1), ],
),
neat=NEAT(
species=DefaultSpecies(
genome=DefaultGenome(
num_inputs=4, # [-1, -1, -1, 0]
num_outputs=1,
max_nodes=50,
max_conns=100,
node_gene=DefaultNodeGene(
activation_default=Act.tanh,
activation_options=(Act.tanh,),
),
),
pop_size=10000,
species_size=10,
compatibility_threshold=3.5,
),
),
activation=Act.sigmoid,
activate_time=10,
),
problem=XOR3d(),
generation_limit=300,
fitness_target=-1e-6
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)
# show result
pipeline.show(state, best)

41
examples/func_fit/xor_recurrent.py
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@@ -1,41 +0,0 @@
from pipeline import Pipeline
from algorithm.neat import *
from problem.func_fit import XOR3d
from utils.activation import ACT_ALL
from utils.aggregation import AGG_ALL
if __name__ == '__main__':
pipeline = Pipeline(
seed=0,
algorithm=NEAT(
species=DefaultSpecies(
genome=RecurrentGenome(
num_inputs=3,
num_outputs=1,
max_nodes=50,
max_conns=100,
activate_time=5,
node_gene=DefaultNodeGene(
activation_options=ACT_ALL,
# aggregation_options=AGG_ALL,
activation_replace_rate=0.2
),
),
pop_size=10000,
species_size=10,
compatibility_threshold=3.5,
),
),
problem=XOR3d(),
generation_limit=10000,
fitness_target=-1e-8
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)
# show result
pipeline.show(state, best)

34
examples/gymnax/arcbot.py
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@@ -1,34 +0,0 @@
import jax.numpy as jnp
from pipeline import Pipeline
from algorithm.neat import *
from problem.rl_env import GymNaxEnv
if __name__ == '__main__':
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
genome=DefaultGenome(
num_inputs=6,
num_outputs=3,
max_nodes=50,
max_conns=100,
output_transform=lambda out: jnp.argmax(out) # the action of acrobot is {0, 1, 2}
),
pop_size=10000,
species_size=10,
),
),
problem=GymNaxEnv(
env_name='Acrobot-v1',
),
generation_limit=10000,
fitness_target=-62
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)

34
examples/gymnax/cartpole.py
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@@ -1,34 +0,0 @@
import jax.numpy as jnp
from pipeline import Pipeline
from algorithm.neat import *
from problem.rl_env import GymNaxEnv
if __name__ == '__main__':
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
genome=DefaultGenome(
num_inputs=4,
num_outputs=2,
max_nodes=50,
max_conns=100,
output_transform=lambda out: jnp.argmax(out) # the action of cartpole is {0, 1}
),
pop_size=10000,
species_size=10,
),
),
problem=GymNaxEnv(
env_name='CartPole-v1',
),
generation_limit=10000,
fitness_target=500
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)

54
examples/gymnax/cartpole_hyperneat.py
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@@ -1,54 +0,0 @@
import jax.numpy as jnp
from config import *
from pipeline import Pipeline
from algorithm import NEAT
from algorithm.neat.gene import NormalGene, NormalGeneConfig
from algorithm.hyperneat import HyperNEAT, NormalSubstrateConfig, NormalSubstrate
from problem.rl_env import GymNaxConfig, GymNaxEnv
def example_conf():
return Config(
basic=BasicConfig(
seed=42,
fitness_target=500,
pop_size=10000
),
neat=NeatConfig(
inputs=4,
outputs=1,
),
gene=NormalGeneConfig(
activation_default=Act.tanh,
activation_options=(Act.tanh,),
),
hyperneat=HyperNeatConfig(
activation=Act.sigmoid,
inputs=4,
outputs=2
),
substrate=NormalSubstrateConfig(
input_coors=((-1, -1), (-0.5, -1), (0, -1), (0.5, -1), (1, -1)),
hidden_coors=(
# (-1, -0.5), (-0.5, -0.5), (0, -0.5), (0.5, -0.5),
(1, 0), (-1, 0), (-0.5, 0), (0, 0), (0.5, 0), (1, 0),
# (1, 0.5), (-1, 0.5), (-0.5, 0.5), (0, 0.5), (0.5, 0.5), (1, 0.5),
),
output_coors=((-1, 1), (1, 1)),
),
problem=GymNaxConfig(
env_name='CartPole-v1',
output_transform=lambda out: jnp.argmax(out) # the action of cartpole is {0, 1}
)
)
if __name__ == '__main__':
conf = example_conf()
algorithm = HyperNEAT(conf, NormalGene, NormalSubstrate)
pipeline = Pipeline(conf, algorithm, GymNaxEnv)
state = pipeline.setup()
pipeline.pre_compile(state)
state, best = pipeline.auto_run(state)

34
examples/gymnax/mountain_car.py
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@@ -1,34 +0,0 @@
import jax.numpy as jnp
from pipeline import Pipeline
from algorithm.neat import *
from problem.rl_env import GymNaxEnv
if __name__ == '__main__':
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
genome=DefaultGenome(
num_inputs=2,
num_outputs=3,
max_nodes=50,
max_conns=100,
output_transform=lambda out: jnp.argmax(out) # the action of mountain car is {0, 1, 2}
),
pop_size=10000,
species_size=10,
),
),
problem=GymNaxEnv(
env_name='MountainCar-v0',
),
generation_limit=10000,
fitness_target=0
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)

36
examples/gymnax/mountain_car_continuous.py
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from pipeline import Pipeline
from algorithm.neat import *
from problem.rl_env import GymNaxEnv
from utils import Act
if __name__ == '__main__':
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
genome=DefaultGenome(
num_inputs=2,
num_outputs=1,
max_nodes=50,
max_conns=100,
node_gene=DefaultNodeGene(
activation_options=(Act.tanh, ),
activation_default=Act.tanh,
)
),
pop_size=10000,
species_size=10,
),
),
problem=GymNaxEnv(
env_name='MountainCarContinuous-v0',
),
generation_limit=10000,
fitness_target=500
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)

37
examples/gymnax/pendulum.py
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@@ -1,37 +0,0 @@
from pipeline import Pipeline
from algorithm.neat import *
from problem.rl_env import GymNaxEnv
from utils import Act
if __name__ == '__main__':
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
genome=DefaultGenome(
num_inputs=3,
num_outputs=1,
max_nodes=50,
max_conns=100,
node_gene=DefaultNodeGene(
activation_options=(Act.tanh,),
activation_default=Act.tanh,
),
output_transform=lambda out: out * 2 # the action of pendulum is [-2, 2]
),
pop_size=10000,
species_size=10,
),
),
problem=GymNaxEnv(
env_name='Pendulum-v1',
),
generation_limit=10000,
fitness_target=0
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)

33
examples/gymnax/reacher.py
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@@ -1,33 +0,0 @@
import jax.numpy as jnp
from pipeline import Pipeline
from algorithm.neat import *
from problem.rl_env import GymNaxEnv
if __name__ == '__main__':
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
genome=DefaultGenome(
num_inputs=8,
num_outputs=2,
max_nodes=50,
max_conns=100,
),
pop_size=10000,
species_size=10,
),
),
problem=GymNaxEnv(
env_name='Reacher-misc',
),
generation_limit=10000,
fitness_target =500
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)