89 lines
2.4 KiB
Markdown
89 lines
2.4 KiB
Markdown
# NEATax: Tensorized NEAT implementation in JAX
|
|
|
|
TensorNEAT is a powerful tool that utilizes JAX to implement the NEAT (NeuroEvolution of Augmenting Topologies)
|
|
algorithm. It provides support for parallel execution of tasks such as network forward computation, mutation,
|
|
and crossover at the population level.
|
|
|
|
## Requirements
|
|
* available [JAX](https://github.com/google/jax#installation) environment;
|
|
* [gymnax](https://github.com/RobertTLange/gymnax) (optional).
|
|
|
|
## Example
|
|
Simple Example for XOR problem:
|
|
```python
|
|
from config import *
|
|
from pipeline import Pipeline
|
|
from algorithm import NEAT
|
|
from algorithm.neat.gene import NormalGene, NormalGeneConfig
|
|
from problem.func_fit import XOR, FuncFitConfig
|
|
|
|
if __name__ == '__main__':
|
|
# running config
|
|
config = Config(
|
|
basic=BasicConfig(
|
|
seed=42,
|
|
fitness_target=-1e-2,
|
|
pop_size=10000
|
|
),
|
|
neat=NeatConfig(
|
|
inputs=2,
|
|
outputs=1
|
|
),
|
|
gene=NormalGeneConfig(),
|
|
problem=FuncFitConfig(
|
|
error_method='rmse'
|
|
)
|
|
)
|
|
# define algorithm: NEAT with NormalGene
|
|
algorithm = NEAT(config, NormalGene)
|
|
# full pipeline
|
|
pipeline = Pipeline(config, algorithm, XOR)
|
|
# initialize state
|
|
state = pipeline.setup()
|
|
# run until terminate
|
|
state, best = pipeline.auto_run(state)
|
|
# show result
|
|
pipeline.show(state, best)
|
|
```
|
|
|
|
Simple Example for RL envs in gymnax(CartPole-v0):
|
|
```python
|
|
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
|
|
|
|
if __name__ == '__main__':
|
|
conf = 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}
|
|
)
|
|
)
|
|
|
|
algorithm = NEAT(conf, NormalGene)
|
|
pipeline = Pipeline(conf, algorithm, GymNaxEnv)
|
|
state = pipeline.setup()
|
|
state, best = pipeline.auto_run(state)
|
|
```
|
|
|
|
`/examples` folder contains more examples.
|
|
|
|
## TO BE COMPLETE...
|