Metadata-Version: 2.1
Name: tensorneat
Version: 0.1.0
Summary: tensorneat
Author-email: Lishuang Wang <wanglishuang22@gmail.com>
License: BSD 3-Clause License
        
        Copyright (c) 2024, EMI-Group
        All rights reserved.
        
        Redistribution and use in source and binary forms, with or without
        modification, are permitted provided that the following conditions are met:
        
        1. Redistributions of source code must retain the above copyright notice, this
           list of conditions and the following disclaimer.
        
        2. Redistributions in binary form must reproduce the above copyright notice,
           this list of conditions and the following disclaimer in the documentation
           and/or other materials provided with the distribution.
        
        3. Neither the name of the copyright holder nor the names of its
           contributors may be used to endorse or promote products derived from
           this software without specific prior written permission.
        
        THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
        AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
        IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
        DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
        FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
        DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
        SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
        CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
        OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
        OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Project-URL: Homepage, https://github.com/EMI-Group/tensorneat
Project-URL: Bug Tracker, https://github.com/EMI-Group/tensorneat/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: brax>=0.10.3
Requires-Dist: jax>=0.4.28
Requires-Dist: gymnax>=0.0.8
Requires-Dist: jaxopt>=0.8.3
Requires-Dist: optax>=0.2.2
Requires-Dist: flax>=0.8.4
Requires-Dist: mujoco>=3.1.4
Requires-Dist: mujoco-mjx>=3.1.4

<h1 align="center">
  <picture>
    <source media="(prefers-color-scheme: dark)" srcset="./imgs/evox_logo_dark.png">
    <source media="(prefers-color-scheme: light)" srcset="./imgs/evox_logo_light.png">
    <a href="https://github.com/EMI-Group/evox">
      <img alt="EvoX Logo" height="50" src="./imgs/evox_logo_light.png">
    </a>
  </picture>
  <br>
</h1>

<p align="center">
🌟 TensorNEAT: Tensorized NEAT Implementation in JAX 🌟
</p>

<p align="center">
  <a href="https://arxiv.org/abs/2404.01817">
    <img src="https://img.shields.io/badge/paper-arxiv-red?style=for-the-badge" alt="TensorNEAT Paper on arXiv">
  </a>
</p>

## Introduction
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](https://github.com/EMI-Group/evox/) framewrok.

## Requirements
Due to the rapid iteration of JAX versions, configuring the runtime environment for TensorNEAT can be challenging. We recommend the following versions for the relevant libraries:

- jax (0.4.28)
- jaxlib (0.4.28+cuda12.cudnn89)
- brax (0.10.3)
- gymnax (0.0.8)
  
We provide detailed JAX-related environment references in [recommend_environment](recommend_environment.txt). If you encounter any issues while configuring the environment yourself, you can use this as a reference.

## Example
Simple Example for XOR problem:
```python
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):
```python
from pipeline import Pipeline
from algorithm.neat import *

from problem.rl_env import BraxEnv
from tensorneat.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](https://github.com/EMI-Group/evox/discussions).
- 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}
}