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complete fully stateful!

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This commit is contained in:
wls2002
2024-05-26 18:08:43 +08:00
parent cf69b916af
commit 18c3d44c79
41 changed files with 620 additions and 495 deletions
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10
tensorneat/examples/brax/ant.py
View File

@@ -4,7 +4,7 @@ from algorithm.neat import *
from problem.rl_env import BraxEnv
from utils import Act
if __name__ == '__main__':
if __name__ == "__main__":
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
@@ -17,21 +17,21 @@ if __name__ == '__main__':
activation_options=(Act.tanh,),
activation_default=Act.tanh,
),
output_transform=Act.tanh
output_transform=Act.tanh,
),
pop_size=1000,
species_size=10,
),
),
problem=BraxEnv(
env_name='ant',
env_name="ant",
),
generation_limit=10000,
fitness_target=5000
fitness_target=5000,
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)
state, best = pipeline.auto_run(state)

10
tensorneat/examples/brax/half_cheetah.py
View File

@@ -4,7 +4,7 @@ from algorithm.neat import *
from problem.rl_env import BraxEnv
from utils import Act
if __name__ == '__main__':
if __name__ == "__main__":
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
@@ -16,21 +16,21 @@ if __name__ == '__main__':
node_gene=DefaultNodeGene(
activation_options=(Act.tanh,),
activation_default=Act.tanh,
)
),
),
pop_size=1000,
species_size=10,
),
),
problem=BraxEnv(
env_name='halfcheetah',
env_name="halfcheetah",
),
generation_limit=10000,
fitness_target=5000
fitness_target=5000,
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)
state, best = pipeline.auto_run(state)

10
tensorneat/examples/brax/reacher.py
View File

@@ -4,7 +4,7 @@ from algorithm.neat import *
from problem.rl_env import BraxEnv
from utils import Act
if __name__ == '__main__':
if __name__ == "__main__":
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
@@ -16,21 +16,21 @@ if __name__ == '__main__':
node_gene=DefaultNodeGene(
activation_options=(Act.tanh,),
activation_default=Act.tanh,
)
),
),
pop_size=100,
species_size=10,
),
),
problem=BraxEnv(
env_name='reacher',
env_name="reacher",
),
generation_limit=10000,
fitness_target=5000
fitness_target=5000,
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)
state, best = pipeline.auto_run(state)

10
tensorneat/examples/brax/walker.py
View File

@@ -4,7 +4,7 @@ from algorithm.neat import *
from problem.rl_env import BraxEnv
from utils import Act
if __name__ == '__main__':
if __name__ == "__main__":
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
@@ -16,21 +16,21 @@ if __name__ == '__main__':
node_gene=DefaultNodeGene(
activation_options=(Act.tanh,),
activation_default=Act.tanh,
)
),
),
pop_size=10000,
species_size=10,
),
),
problem=BraxEnv(
env_name='walker2d',
env_name="walker2d",
),
generation_limit=10000,
fitness_target=5000
fitness_target=5000,
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)
state, best = pipeline.auto_run(state)

16
tensorneat/examples/func_fit/xor.py
View File

@@ -4,7 +4,7 @@ from algorithm.neat import *
from problem.func_fit import XOR3d
from utils import Act
if __name__ == '__main__':
if __name__ == "__main__":
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
@@ -18,22 +18,22 @@ if __name__ == '__main__':
activation_options=(Act.tanh,),
),
output_transform=Act.sigmoid, # the activation function for output node
mutation=DefaultMutation(
node_add=0.05,
conn_add=0.2,
node_delete=0,
conn_delete=0,
),
),
pop_size=10000,
species_size=10,
compatibility_threshold=3.5,
survival_threshold=0.01, # magic
),
mutation=DefaultMutation(
node_add=0.05,
conn_add=0.2,
node_delete=0,
conn_delete=0,
)
),
problem=XOR3d(),
generation_limit=10000,
fitness_target=-1e-8
fitness_target=-1e-8,
)
# initialize state

23
tensorneat/examples/func_fit/xor3d_hyperneat.py
View File

@@ -5,17 +5,28 @@ from utils import Act
from problem.func_fit import XOR3d
if __name__ == '__main__':
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),
(-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),
],
output_coors=[(0, 1), ],
),
neat=NEAT(
species=DefaultSpecies(
@@ -42,7 +53,7 @@ if __name__ == '__main__':
),
problem=XOR3d(),
generation_limit=300,
fitness_target=-1e-6
fitness_target=-1e-6,
)
# initialize state

24
tensorneat/examples/func_fit/xor_recurrent.py
View File

@@ -1,10 +1,11 @@
from pipeline import Pipeline
from algorithm.neat import *
from algorithm.neat.gene.node.default_without_response import NodeGeneWithoutResponse
from problem.func_fit import XOR3d
from utils.activation import ACT_ALL, Act
if __name__ == '__main__':
if __name__ == "__main__":
pipeline = Pipeline(
seed=0,
algorithm=NEAT(
@@ -15,27 +16,26 @@ if __name__ == '__main__':
max_nodes=50,
max_conns=100,
activate_time=5,
node_gene=DefaultNodeGene(
activation_options=ACT_ALL,
activation_replace_rate=0.2
node_gene=NodeGeneWithoutResponse(
activation_options=ACT_ALL, activation_replace_rate=0.2
),
output_transform=Act.sigmoid,
mutation=DefaultMutation(
node_add=0.05,
conn_add=0.2,
node_delete=0,
conn_delete=0,
),
output_transform=Act.sigmoid
),
pop_size=10000,
species_size=10,
compatibility_threshold=3.5,
survival_threshold=0.03,
),
mutation=DefaultMutation(
node_add=0.05,
conn_add=0.2,
node_delete=0,
conn_delete=0,
)
),
problem=XOR3d(),
generation_limit=10000,
fitness_target=-1e-8
fitness_target=-1e-8,
)
# initialize state

12
tensorneat/examples/gymnax/arcbot.py
View File

@@ -5,7 +5,7 @@ from algorithm.neat import *
from problem.rl_env import GymNaxEnv
if __name__ == '__main__':
if __name__ == "__main__":
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
@@ -14,21 +14,23 @@ if __name__ == '__main__':
num_outputs=3,
max_nodes=50,
max_conns=100,
output_transform=lambda out: jnp.argmax(out) # the action of acrobot is {0, 1, 2}
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',
env_name="Acrobot-v1",
),
generation_limit=10000,
fitness_target=-62
fitness_target=-62,
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)
state, best = pipeline.auto_run(state)

12
tensorneat/examples/gymnax/cartpole.py
View File

@@ -5,7 +5,7 @@ from algorithm.neat import *
from problem.rl_env import GymNaxEnv
if __name__ == '__main__':
if __name__ == "__main__":
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
@@ -14,21 +14,23 @@ if __name__ == '__main__':
num_outputs=2,
max_nodes=50,
max_conns=100,
output_transform=lambda out: jnp.argmax(out) # the action of cartpole is {0, 1}
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',
env_name="CartPole-v1",
),
generation_limit=10000,
fitness_target=500
fitness_target=500,
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)
state, best = pipeline.auto_run(state)

29
tensorneat/examples/gymnax/cartpole_hyperneat.py
View File

@@ -10,11 +10,7 @@ from problem.rl_env import GymNaxConfig, GymNaxEnv
def example_conf():
return Config(
basic=BasicConfig(
seed=42,
fitness_target=500,
pop_size=10000
),
basic=BasicConfig(seed=42, fitness_target=500, pop_size=10000),
neat=NeatConfig(
inputs=4,
outputs=1,
@@ -23,28 +19,31 @@ def example_conf():
activation_default=Act.tanh,
activation_options=(Act.tanh,),
),
hyperneat=HyperNeatConfig(
activation=Act.sigmoid,
inputs=4,
outputs=2
),
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),
(-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}
)
env_name="CartPole-v1",
output_transform=lambda out: jnp.argmax(
out
), # the action of cartpole is {0, 1}
),
)
if __name__ == '__main__':
if __name__ == "__main__":
conf = example_conf()
algorithm = HyperNEAT(conf, NormalGene, NormalSubstrate)

12
tensorneat/examples/gymnax/mountain_car.py
View File

@@ -5,7 +5,7 @@ from algorithm.neat import *
from problem.rl_env import GymNaxEnv
if __name__ == '__main__':
if __name__ == "__main__":
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
@@ -14,21 +14,23 @@ if __name__ == '__main__':
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}
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',
env_name="MountainCar-v0",
),
generation_limit=10000,
fitness_target=0
fitness_target=0,
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)
state, best = pipeline.auto_run(state)

12
tensorneat/examples/gymnax/mountain_car_continuous.py
View File

@@ -4,7 +4,7 @@ from algorithm.neat import *
from problem.rl_env import GymNaxEnv
from utils import Act
if __name__ == '__main__':
if __name__ == "__main__":
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
@@ -14,23 +14,23 @@ if __name__ == '__main__':
max_nodes=50,
max_conns=100,
node_gene=DefaultNodeGene(
activation_options=(Act.tanh, ),
activation_options=(Act.tanh,),
activation_default=Act.tanh,
)
),
),
pop_size=10000,
species_size=10,
),
),
problem=GymNaxEnv(
env_name='MountainCarContinuous-v0',
env_name="MountainCarContinuous-v0",
),
generation_limit=10000,
fitness_target=500
fitness_target=500,
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)
state, best = pipeline.auto_run(state)

11
tensorneat/examples/gymnax/pendulum.py
View File

@@ -4,7 +4,7 @@ from algorithm.neat import *
from problem.rl_env import GymNaxEnv
from utils import Act
if __name__ == '__main__':
if __name__ == "__main__":
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
@@ -17,21 +17,22 @@ if __name__ == '__main__':
activation_options=(Act.tanh,),
activation_default=Act.tanh,
),
output_transform=lambda out: out * 2 # the action of pendulum is [-2, 2]
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',
env_name="Pendulum-v1",
),
generation_limit=10000,
fitness_target=0
fitness_target=0,
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)
state, best = pipeline.auto_run(state)

8
tensorneat/examples/gymnax/reacher.py
View File

@@ -5,7 +5,7 @@ from algorithm.neat import *
from problem.rl_env import GymNaxEnv
if __name__ == '__main__':
if __name__ == "__main__":
pipeline = Pipeline(
algorithm=NEAT(
species=DefaultSpecies(
@@ -20,14 +20,14 @@ if __name__ == '__main__':
),
),
problem=GymNaxEnv(
env_name='Reacher-misc',
env_name="Reacher-misc",
),
generation_limit=10000,
fitness_target =500
fitness_target=500,
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)
state, best = pipeline.auto_run(state)

1
tensorneat/examples/with_evox/ray_test.py
View File

@@ -1,4 +1,5 @@
import ray
ray.init(num_gpus=2)
available_resources = ray.available_resources()