tensorneat-mend

TensorNEAT is a JAX-based libaray for NeuroEvolution of Augmenting Topologies (NEAT) algorithms, 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. TensorNEAT is compatible with the EvoX framewrok.

Key Features

JAX-based network for neuroevolution:
- Batch inference across networks with different architectures, GPU-accelerated.
- Evolve networks with irregular structures and fully customize their behavior.
- Visualize the network and represent it in mathematical formulas or codes.
GPU-accelerated NEAT implementation:
- Run NEAT and HyperNEAT on GPUs.
- Achieve 500x speedup compared to CPU-based NEAT libraries.
Rich in extended content:
- Compatible with EvoX for multi-device and distributed support.
- Test neuroevolution algorithms on advanced RL tasks (Brax, Gymnax).

Installation

Install tensorneat from the GitHub source code:

pip install git+https://github.com/EMI-Group/tensorneat.git

Basic API Usage

Start your journey with TensorNEAT in a few simple steps:

Import necessary modules:

from tensorneat.pipeline import Pipeline
from tensorneat import algorithm, genome, problem, common

Configure the NEAT algorithm and define a problem:

algorithm = algorithm.NEAT(
    pop_size=10000,
    species_size=20,
    survival_threshold=0.01,
    genome=genome.DefaultGenome(
        num_inputs=3,
        num_outputs=1,
        output_transform=common.ACT.sigmoid,
    ),
)
problem = problem.XOR3d()

Initialize the pipeline and run:

pipeline = Pipeline(
    algorithm,
    problem,
    generation_limit=200,
    fitness_target=-1e-6,
    seed=42,
)
state = pipeline.setup()
# run until termination
state, best = pipeline.auto_run(state)
# show results
pipeline.show(state, best)

Obtain result in a few generations:

Fitness limit reached!
input: [0. 0. 0.], target: [0.], predict: [0.00037953]
input: [0. 0. 1.], target: [1.], predict: [0.9990619]
input: [0. 1. 0.], target: [1.], predict: [0.9991497]
input: [0. 1. 1.], target: [0.], predict: [0.0004661]
input: [1. 0. 0.], target: [1.], predict: [0.998262]
input: [1. 0. 1.], target: [0.], predict: [0.00077246]
input: [1. 1. 0.], target: [0.], predict: [0.00082464]
input: [1. 1. 1.], target: [1.], predict: [0.99909043]
loss: 8.861396736392635e-07

Visualize the best network:

network = algorithm.genome.network_dict(state, *best)
algorithm.genome.visualize(network, save_path="./imgs/xor_network.svg")

Transform the network to latex formulas or python codes:

from tensorneat.common.sympy_tools import to_latex_code, to_python_code

sympy_res = algorithm.genome.sympy_func(
    state, network, sympy_output_transform=ACT.obtain_sympy(ACT.sigmoid)
)
latex_code = to_latex_code(*sympy_res)
print(latex_code)

python_code = to_python_code(*sympy_res)
print(python_code)

Latex formulas:

\begin{align}
h_{0} &= \frac{1}{2.83 e^{5.66 h_{1} - 6.08 h_{2} - 3.03 i_{2}} + 1}\newline
h_{1} &= \frac{1}{0.3 e^{- 4.8 h_{2} + 9.22 i_{0} + 8.09 i_{1} - 10.24 i_{2}} + 1}\newline
h_{2} &= \frac{1}{0.27 e^{4.28 i_{1}} + 1}\newline
o_{0} &= \frac{1}{0.68 e^{- 20.86 h_{0} + 11.12 h_{1} + 14.22 i_{0} - 1.96 i_{2}} + 1}\newline
\end{align}

Python codes:

h = np.zeros(3)
o = np.zeros(1)
h[0] = 1/(2.825013*exp(5.660946*h[1] - 6.083459*h[2] - 3.033361*i[2]) + 1)
h[1] = 1/(0.300038*exp(-4.802896*h[2] + 9.215506*i[0] + 8.091845*i[1] - 10.241107*i[2]) + 1)
h[2] = 1/(0.269965*exp(4.279962*i[1]) + 1)
o[0] = 1/(0.679321*exp(-20.860441*h[0] + 11.122242*h[1] + 14.216276*i[0] - 1.961642*i[2]) + 1)

Multi-device and Distributed Acceleration

TensorNEAT doesn't natively support multi-device or distributed execution, but these features can be accessed via the EvoX framework. EvoX is a high-performance, distributed, GPU-accelerated framework for Evolutionary Algorithms. For more details, visit: EvoX GitHub.

TensorNEAT includes an EvoX Adaptor, which allows TensorNEAT algorithms to run within the EvoX framework. Additionally, TensorNEAT provides a monitor for use with EvoX.

Here is an example of creating an EvoX algorithm and monitor:

from tensorneat.common.evox_adaptors import EvoXAlgorithmAdaptor, TensorNEATMonitor
from tensorneat.algorithm import NEAT
from tensorneat.genome import DefaultGenome, BiasNode
from tensorneat.common import ACT, AGG

# define algorithm in TensorNEAT
neat_algorithm = NEAT(
    pop_size=1000,
    species_size=20,
    survival_threshold=0.1,
    compatibility_threshold=1.0,
    genome=DefaultGenome(
        max_nodes=50,
        max_conns=200,
        num_inputs=17,
        num_outputs=6,
        node_gene=BiasNode(
            activation_options=ACT.tanh,
            aggregation_options=AGG.sum,
        ),
        output_transform=ACT.tanh,
    ),
)
# use adaptor to create EvoX algorithm
evox_algorithm = EvoXAlgorithmAdaptor(neat_algorithm)
# monitor in Evox
monitor = TensorNEATMonitor(neat_algorithm, is_save=False)

Using this code, you can run the NEAT algorithm within EvoX and leverage EvoX's multi-device and distributed capabilities.

For a complete example, see ./example/with_evox/walker2d_evox.py, which demonstrates EvoX's multi-device functionality.

Community & Support

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

Citing TensorNEAT

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

@article{tensorneat,
  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 (GECCO)},
  year = {2024}
}