TensorNEAT: Tensorized NEAT implementation in JAX

Introduction

🚀TensorNEAT is an adaptation of the NeuroEvolution of Augmenting Topologies (NEAT) algorithm, focused on harnessing GPU acceleration to enhance the efficiency of evolving neural network structures for complex tasks. Its core mechanism involves the tensorization of network topologies, enabling parallel processing and significantly boosting computational speed and scalability by leveraging modern hardware accelerators. TensorRVEA is compatible with the EvoX framewrok.

Requirements

TensorNEAT requires:

jax (version >= 0.4.16)
jaxlib (version >= 0.3.0)
brax [optional]
gymnax [optional]

Example

Simple Example for XOR problem:

from pipeline import Pipeline
from algorithm.neat import *

from problem.func_fit import XOR3d

if __name__ == '__main__':
    pipeline = Pipeline(
        algorithm=NEAT(
            species=DefaultSpecies(
                genome=DefaultGenome(
                    num_inputs=3,
                    num_outputs=1,
                    max_nodes=50,
                    max_conns=100,
                ),
                pop_size=10000,
                species_size=10,
                compatibility_threshold=3.5,
            ),
        ),
        problem=XOR3d(),
        generation_limit=10000,
        fitness_target=-1e-8
    )

    # initialize state
    state = pipeline.setup()
    # print(state)
    # run until terminate
    state, best = pipeline.auto_run(state)
    # show result
    pipeline.show(state, best)

Simple Example for RL envs in Brax (Ant):

from pipeline import Pipeline
from algorithm.neat import *

from problem.rl_env import BraxEnv
from utils import Act

if __name__ == '__main__':
    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
    )

    # initialize state
    state = pipeline.setup()
    # print(state)
    # run until terminate
    state, best = pipeline.auto_run(state)

more examples are in tensorneat/examples.

Community & Support

Engage in discussions and share your experiences on GitHub Discussion Board.
Join our QQ group (ID: 297969717).
Official Website: https://evox.group/

Citing TensorRVEA

If you use TensorNEAT in your research and want to cite it in your work, please use:

@article{tensorrvea,
  title = {{Tensorized} {NeuroEvolution} of {Augmenting} {Topologies} for {GPU} {Acceleration}},
  author = {Wang, Lishuang and Zhao, Mengfei and Liu, Enyu and Sun, Kebin and Cheng, Ran},
  booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
  year = {2024},
  series = {GECCO '24}
}

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