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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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62
examples/brax/ant.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 BraxEnv, BraxConfig
def example_conf():
return Config(
basic=BasicConfig(
seed=42,
fitness_target=10000,
pop_size=100
),
neat=NeatConfig(
inputs=27,
outputs=8,
),
gene=NormalGeneConfig(
activation_default=Act.tanh,
activation_options=(Act.tanh,),
),
problem=BraxConfig(
env_name="ant"
)
)
from algorithm.neat import *
from problem.rl_env import BraxEnv
from utils import Act
if __name__ == '__main__':
conf = example_conf()
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
)
algorithm = NEAT(conf, NormalGene)
pipeline = Pipeline(conf, algorithm, BraxEnv)
# 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)

68
examples/brax/half_cheetah.py
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@@ -1,42 +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 BraxEnv, BraxConfig
# ['ant', 'halfcheetah', 'hopper', 'humanoid', 'humanoidstandup', 'inverted_pendulum', 'inverted_double_pendulum', 'pusher', 'reacher', 'walker2d']
def example_conf():
return Config(
basic=BasicConfig(
seed=42,
fitness_target=10000,
generation_limit=10,
pop_size=100
),
neat=NeatConfig(
inputs=17,
outputs=6,
),
gene=NormalGeneConfig(
activation_default=Act.tanh,
activation_options=(Act.tanh,),
),
problem=BraxConfig(
env_name="halfcheetah"
)
)
from algorithm.neat import *
from problem.rl_env import BraxEnv
from utils import Act
if __name__ == '__main__':
conf = example_conf()
algorithm = NEAT(conf, NormalGene)
pipeline = Pipeline(conf, algorithm, BraxEnv)
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()
pipeline.pre_compile(state)
state, best = pipeline.auto_run(state)
pipeline.show(state, best, save_path="half_cheetah.gif", )
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)

62
examples/brax/reacher.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 BraxEnv, BraxConfig
def example_conf():
return Config(
basic=BasicConfig(
seed=42,
fitness_target=10000,
pop_size=1000
),
neat=NeatConfig(
inputs=11,
outputs=2,
),
gene=NormalGeneConfig(
activation_default=Act.tanh,
activation_options=(Act.tanh,),
),
problem=BraxConfig(
env_name="reacher"
)
)
from algorithm.neat import *
from problem.rl_env import BraxEnv
from utils import Act
if __name__ == '__main__':
conf = example_conf()
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
)
algorithm = NEAT(conf, NormalGene)
pipeline = Pipeline(conf, algorithm, BraxEnv)
# 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)