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update HyperNEAT;

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All example can currently run!
This commit is contained in:
wls2002
2024-05-26 19:51:22 +08:00
parent 18c3d44c79
commit 9f6154d128
15 changed files with 112 additions and 78 deletions
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33
tensorneat/algorithm/hyperneat/hyperneat.py
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@@ -40,19 +40,22 @@ class HyperNEAT(BaseAlgorithm):
output_transform=output_transform,
)
def setup(self, randkey):
return State(neat_state=self.neat.setup(randkey))
def setup(self, state=State()):
state = self.neat.setup(state)
state = self.substrate.setup(state)
return self.hyper_genome.setup(state)
def ask(self, state: State):
return self.neat.ask(state.neat_state)
return self.neat.ask(state)
def tell(self, state: State, fitness):
return state.update(neat_state=self.neat.tell(state.neat_state, fitness))
state = self.neat.tell(state, fitness)
return state
def transform(self, individual):
transformed = self.neat.transform(individual)
query_res = jax.vmap(self.neat.forward, in_axes=(0, None))(
self.substrate.query_coors, transformed
def transform(self, state, individual):
transformed = self.neat.transform(state, individual)
query_res = jax.vmap(self.neat.forward, in_axes=(None, 0, None))(
state, self.substrate.query_coors, transformed
)
# mute the connection with weight below threshold
@@ -74,12 +77,12 @@ class HyperNEAT(BaseAlgorithm):
h_nodes, h_conns = self.substrate.make_nodes(
query_res
), self.substrate.make_conn(query_res)
return self.hyper_genome.transform(h_nodes, h_conns)
return self.hyper_genome.transform(state, h_nodes, h_conns)
def forward(self, inputs, transformed):
def forward(self, state, inputs, transformed):
# add bias
inputs_with_bias = jnp.concatenate([inputs, jnp.array([1])])
return self.hyper_genome.forward(inputs_with_bias, transformed)
return self.hyper_genome.forward(state, inputs_with_bias, transformed)
@property
def num_inputs(self):
@@ -94,10 +97,10 @@ class HyperNEAT(BaseAlgorithm):
return self.neat.pop_size
def member_count(self, state: State):
return self.neat.member_count(state.neat_state)
return self.neat.member_count(state)
def generation(self, state: State):
return self.neat.generation(state.neat_state)
return self.neat.generation(state)
class HyperNodeGene(BaseNodeGene):
@@ -110,7 +113,7 @@ class HyperNodeGene(BaseNodeGene):
self.activation = activation
self.aggregation = aggregation
def forward(self, attrs, inputs, is_output_node=False):
def forward(self, state, attrs, inputs, is_output_node=False):
return jax.lax.cond(
is_output_node,
lambda: self.aggregation(inputs), # output node does not need activation
@@ -121,6 +124,6 @@ class HyperNodeGene(BaseNodeGene):
class HyperNEATConnGene(BaseConnGene):
custom_attrs = ["weight"]
def forward(self, attrs, inputs):
def forward(self, state, attrs, inputs):
weight = attrs[0]
return inputs * weight

6
tensorneat/algorithm/hyperneat/substrate/base.py
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@@ -1,4 +1,10 @@
from utils import State
class BaseSubstrate:
def setup(self, state=State()):
return state
def make_nodes(self, query_res):
raise NotImplementedError

6
tensorneat/examples/brax/ant.py
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@@ -11,8 +11,8 @@ if __name__ == "__main__":
genome=DefaultGenome(
num_inputs=27,
num_outputs=8,
max_nodes=50,
max_conns=100,
max_nodes=100,
max_conns=200,
node_gene=DefaultNodeGene(
activation_options=(Act.tanh,),
activation_default=Act.tanh,
@@ -21,6 +21,8 @@ if __name__ == "__main__":
),
pop_size=1000,
species_size=10,
compatibility_threshold=3.5,
survival_threshold=0.01,
),
),
problem=BraxEnv(

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1
tensorneat/examples/brax/half_cheetah.py
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@@ -17,6 +17,7 @@ if __name__ == "__main__":
activation_options=(Act.tanh,),
activation_default=Act.tanh,
),
output_transform=Act.tanh
),
pop_size=1000,
species_size=10,

1
tensorneat/examples/brax/reacher.py
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@@ -17,6 +17,7 @@ if __name__ == "__main__":
activation_options=(Act.tanh,),
activation_default=Act.tanh,
),
output_transform=Act.tanh,
),
pop_size=100,
species_size=10,

1
tensorneat/examples/brax/walker.py
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@@ -17,6 +17,7 @@ if __name__ == "__main__":
activation_options=(Act.tanh,),
activation_default=Act.tanh,
),
output_transform=Act.tanh
),
pop_size=10000,
species_size=10,

12
tensorneat/examples/func_fit/xor3d_hyperneat.py
View File

@@ -9,11 +9,9 @@ if __name__ == "__main__":
pipeline = Pipeline(
algorithm=HyperNEAT(
substrate=FullSubstrate(
input_coors=[(-1, -1), (0.333, -1), (-0.333, -1), (1, -1)],
input_coors=[(-1, -1), (0.333, -1), (-0.333, -1), (1, -1)], # 3(XOR3d inputs) + 1(bias)
hidden_coors=[
(-1, -0.5),
(0.333, -0.5),
(-0.333, -0.5),
(-1, -0.5), (0.333, -0.5), (-0.333, -0.5),
(1, -0.5),
(-1, 0),
(0.333, 0),
@@ -25,14 +23,14 @@ if __name__ == "__main__":
(1, 0.5),
],
output_coors=[
(0, 1),
(0, 1), # one output
],
),
neat=NEAT(
species=DefaultSpecies(
genome=DefaultGenome(
num_inputs=4, # [-1, -1, -1, 0]
num_outputs=1,
num_inputs=4, # [*coor1, *coor2]
num_outputs=1, # the weight of connection between two coor1 and coor2
max_nodes=50,
max_conns=100,
node_gene=DefaultNodeGene(

111
tensorneat/examples/gymnax/cartpole_hyperneat.py
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@@ -1,53 +1,74 @@
import jax.numpy as jnp
import jax
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}
),
)
from algorithm.neat import *
from algorithm.hyperneat import *
from utils import Act
from problem.rl_env import GymNaxEnv
if __name__ == "__main__":
conf = example_conf()
pipeline = Pipeline(
algorithm=HyperNEAT(
substrate=FullSubstrate(
input_coors=[
(-1, -1),
(-0.5, -1),
(0, -1),
(0.5, -1),
(1, -1),
], # 4(problem inputs) + 1(bias)
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=[
(-1, 1),
(1, 1), # one output
],
),
neat=NEAT(
species=DefaultSpecies(
genome=DefaultGenome(
num_inputs=4, # [*coor1, *coor2]
num_outputs=1, # the weight of connection between two coor1 and coor2
max_nodes=50,
max_conns=100,
node_gene=DefaultNodeGene(
activation_default=Act.tanh,
activation_options=(Act.tanh,),
),
output_transform=Act.tanh, # the activation function for output node in NEAT
),
pop_size=10000,
species_size=10,
compatibility_threshold=3.5,
survival_threshold=0.03,
),
),
activation=Act.tanh, # the activation function for output node in HyperNEAT
activate_time=10,
output_transform=jax.numpy.argmax, # action of cartpole is in {0, 1}
),
problem=GymNaxEnv(
env_name="CartPole-v1",
),
generation_limit=300,
fitness_target=500,
)
algorithm = HyperNEAT(conf, NormalGene, NormalSubstrate)
pipeline = Pipeline(conf, algorithm, GymNaxEnv)
# initialize state
state = pipeline.setup()
pipeline.pre_compile(state)
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)

2
tensorneat/examples/gymnax/mountain_car.py
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@@ -26,7 +26,7 @@ if __name__ == "__main__":
env_name="MountainCar-v0",
),
generation_limit=10000,
fitness_target=0,
fitness_target=-86,
)
# initialize state

3
tensorneat/examples/gymnax/mountain_car_continuous.py
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@@ -17,6 +17,7 @@ if __name__ == "__main__":
activation_options=(Act.tanh,),
activation_default=Act.tanh,
),
output_transform=Act.tanh
),
pop_size=10000,
species_size=10,
@@ -26,7 +27,7 @@ if __name__ == "__main__":
env_name="MountainCarContinuous-v0",
),
generation_limit=10000,
fitness_target=500,
fitness_target=99,
)
# initialize state

4
tensorneat/examples/gymnax/pendulum.py
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@@ -17,7 +17,7 @@ if __name__ == "__main__":
activation_options=(Act.tanh,),
activation_default=Act.tanh,
),
output_transform=lambda out: out
output_transform=lambda out: Act.tanh(out)
* 2, # the action of pendulum is [-2, 2]
),
pop_size=10000,
@@ -28,7 +28,7 @@ if __name__ == "__main__":
env_name="Pendulum-v1",
),
generation_limit=10000,
fitness_target=0,
fitness_target=-10,
)
# initialize state

2
tensorneat/examples/gymnax/reacher.py
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@@ -23,7 +23,7 @@ if __name__ == "__main__":
env_name="Reacher-misc",
),
generation_limit=10000,
fitness_target=500,
fitness_target=90,
)
# initialize state

4
tensorneat/problem/rl_env/brax_env.py
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@@ -5,8 +5,8 @@ from .rl_jit import RLEnv
class BraxEnv(RLEnv):
def __init__(self, env_name: str = "ant", backend: str = "generalized"):
super().__init__()
def __init__(self, max_step=1000, env_name: str = "ant", backend: str = "generalized"):
super().__init__(max_step)
self.env = envs.create(env_name=env_name, backend=backend)
def env_step(self, randkey, env_state, action):

4
tensorneat/problem/rl_env/gymnax_env.py
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@@ -4,8 +4,8 @@ from .rl_jit import RLEnv
class GymNaxEnv(RLEnv):
def __init__(self, env_name):
super().__init__()
def __init__(self, env_name, max_step=1000):
super().__init__(max_step)
assert env_name in gymnax.registered_envs, f"Env {env_name} not registered"
self.env, self.env_params = gymnax.make(env_name)