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finish all refactoring

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This commit is contained in:
wls2002
2024-02-21 15:41:08 +08:00
parent aac41a089d
commit 6970e6a6d5
44 changed files with 856 additions and 825 deletions
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39
examples/gymnax/acrobot.py
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@@ -1,39 +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 problem.rl_env import GymNaxConfig, GymNaxEnv
def example_conf():
return Config(
basic=BasicConfig(
seed=42,
fitness_target=0,
pop_size=10000
),
neat=NeatConfig(
inputs=6,
outputs=3,
),
gene=NormalGeneConfig(
activation_default=Act.tanh,
activation_options=(Act.tanh,),
),
problem=GymNaxConfig(
env_name='Acrobot-v1',
output_transform=lambda out: jnp.argmax(out) # the action of acrobot is {0, 1, 2}
)
)
if __name__ == '__main__':
conf = example_conf()
algorithm = NEAT(conf, NormalGene)
pipeline = Pipeline(conf, algorithm, GymNaxEnv)
state = pipeline.setup()
pipeline.pre_compile(state)
state, best = pipeline.auto_run(state)

34
examples/gymnax/arcbot.py Normal file
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@@ -0,0 +1,34 @@
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)

102
examples/gymnax/cartpole.py
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@@ -1,84 +1,34 @@
import jax.numpy as jnp
from config import *
from pipeline import Pipeline
from algorithm import NEAT
from algorithm.neat.gene import NormalGene, NormalGeneConfig
from problem.rl_env import GymNaxConfig, GymNaxEnv
def example_conf1():
return Config(
basic=BasicConfig(
seed=42,
fitness_target=500,
pop_size=10000
),
neat=NeatConfig(
inputs=4,
outputs=1,
),
gene=NormalGeneConfig(
activation_default=Act.sigmoid,
activation_options=(Act.sigmoid,),
),
problem=GymNaxConfig(
env_name='CartPole-v1',
output_transform=lambda out: jnp.where(out[0] > 0.5, 1, 0) # the action of cartpole is {0, 1}
)
)
def example_conf2():
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,),
),
problem=GymNaxConfig(
env_name='CartPole-v1',
output_transform=lambda out: jnp.where(out[0] > 0, 1, 0) # the action of cartpole is {0, 1}
)
)
def example_conf3():
return Config(
basic=BasicConfig(
seed=42,
fitness_target=501,
pop_size=10000
),
neat=NeatConfig(
inputs=4,
outputs=2,
),
gene=NormalGeneConfig(
activation_default=Act.tanh,
activation_options=(Act.tanh,),
),
problem=GymNaxConfig(
env_name='CartPole-v1',
output_transform=lambda out: jnp.argmax(out) # the action of cartpole is {0, 1}
)
)
from algorithm.neat import *
from problem.rl_env import GymNaxEnv
if __name__ == '__main__':
# all config files above can solve cartpole
conf = example_conf3()
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
)
algorithm = NEAT(conf, NormalGene)
pipeline = Pipeline(conf, algorithm, GymNaxEnv)
# initialize state
state = pipeline.setup()
pipeline.pre_compile(state)
state, best = pipeline.auto_run(state)
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)

57
examples/gymnax/mountain_car.py
View File

@@ -1,39 +1,34 @@
import jax.numpy as jnp
from config import *
from pipeline import Pipeline
from algorithm import NEAT
from algorithm.neat.gene import NormalGene, NormalGeneConfig
from problem.rl_env import GymNaxConfig, GymNaxEnv
def example_conf():
return Config(
basic=BasicConfig(
seed=42,
fitness_target=0,
pop_size=10000
),
neat=NeatConfig(
inputs=2,
outputs=3,
),
gene=NormalGeneConfig(
activation_default=Act.sigmoid,
activation_options=(Act.sigmoid,),
),
problem=GymNaxConfig(
env_name='MountainCar-v0',
output_transform=lambda out: jnp.argmax(out) # the action of cartpole is {0, 1, 2}
)
)
from algorithm.neat import *
from problem.rl_env import GymNaxEnv
if __name__ == '__main__':
conf = example_conf()
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
)
algorithm = NEAT(conf, NormalGene)
pipeline = Pipeline(conf, algorithm, GymNaxEnv)
# initialize state
state = pipeline.setup()
pipeline.pre_compile(state)
state, best = pipeline.auto_run(state)
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)

62
examples/gymnax/mountain_car_continuous.py
View File

@@ -1,38 +1,36 @@
import jax.numpy as jnp
from config import *
from pipeline import Pipeline
from algorithm import NEAT
from algorithm.neat.gene import NormalGene, NormalGeneConfig
from problem.rl_env import GymNaxConfig, GymNaxEnv
def example_conf():
return Config(
basic=BasicConfig(
seed=42,
fitness_target=100,
pop_size=10000
),
neat=NeatConfig(
inputs=2,
outputs=1,
),
gene=NormalGeneConfig(
activation_default=Act.tanh,
activation_options=(Act.tanh,),
),
problem=GymNaxConfig(
env_name='MountainCarContinuous-v0'
)
)
from algorithm.neat import *
from problem.rl_env import GymNaxEnv
from utils import Act
if __name__ == '__main__':
conf = example_conf()
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
)
algorithm = NEAT(conf, NormalGene)
pipeline = Pipeline(conf, algorithm, GymNaxEnv)
# initialize state
state = pipeline.setup()
pipeline.pre_compile(state)
state, best = pipeline.auto_run(state)
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)

65
examples/gymnax/pendulum.py
View File

@@ -1,40 +1,37 @@
import jax.numpy as jnp
from config import *
from pipeline import Pipeline
from algorithm import NEAT
from algorithm.neat.gene import NormalGene, NormalGeneConfig
from problem.rl_env import GymNaxConfig, GymNaxEnv
def example_conf():
return Config(
basic=BasicConfig(
seed=42,
fitness_target=0,
pop_size=10000
),
neat=NeatConfig(
inputs=3,
outputs=1,
),
gene=NormalGeneConfig(
activation_default=Act.tanh,
activation_options=(Act.tanh,),
),
problem=GymNaxConfig(
env_name='Pendulum-v1',
output_transform=lambda out: out * 2 # the action of pendulum is [-2, 2]
)
)
from algorithm.neat import *
from problem.rl_env import GymNaxEnv
from utils import Act
if __name__ == '__main__':
conf = example_conf()
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
)
algorithm = NEAT(conf, NormalGene)
pipeline = Pipeline(conf, algorithm, GymNaxEnv)
# initialize state
state = pipeline.setup()
pipeline.pre_compile(state)
state, best = pipeline.auto_run(state)
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)

57
examples/gymnax/reacher.py
View File

@@ -1,36 +1,33 @@
from config import *
import jax.numpy as jnp
from pipeline import Pipeline
from algorithm import NEAT
from algorithm.neat.gene import NormalGene, NormalGeneConfig
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=8,
outputs=2,
),
gene=NormalGeneConfig(
activation_default=Act.sigmoid,
activation_options=(Act.sigmoid,),
),
problem=GymNaxConfig(
env_name='Reacher-misc',
)
)
from algorithm.neat import *
from problem.rl_env import GymNaxEnv
if __name__ == '__main__':
conf = example_conf()
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
)
algorithm = NEAT(conf, NormalGene)
pipeline = Pipeline(conf, algorithm, GymNaxEnv)
# initialize state
state = pipeline.setup()
pipeline.pre_compile(state)
state, best = pipeline.auto_run(state)
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)