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@@ -36,6 +36,12 @@ TensorNEAT is a JAX-based libaray for NeuroEvolution of Augmenting Topologies (N
- Compatible with **EvoX** for multi-device and distributed support. - Compatible with **EvoX** for multi-device and distributed support.
- Test neuroevolution algorithms on advanced **RL tasks** (Brax, Gymnax). - 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 ## Basic API Usage
Start your journey with TensorNEAT in a few simple steps: Start your journey with TensorNEAT in a few simple steps:
@@ -110,7 +116,7 @@ print(latex_code)
python_code = to_python_code(*sympy_res) python_code = to_python_code(*sympy_res)
print(python_code) print(python_code)
``` ```
Obtain latex formulas: Latex formulas:
```latex ```latex
\begin{align} \begin{align}
h_{0} &= \frac{1}{2.83 e^{5.66 h_{1} - 6.08 h_{2} - 3.03 i_{2}} + 1}\newline h_{0} &= \frac{1}{2.83 e^{5.66 h_{1} - 6.08 h_{2} - 3.03 i_{2}} + 1}\newline
@@ -119,7 +125,7 @@ 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 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} \end{align}
``` ```
Obtain python codes: Python codes:
```python ```python
h = np.zeros(3) h = np.zeros(3)
o = np.zeros(1) o = np.zeros(1)
@@ -129,12 +135,45 @@ 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) 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](https://github.com/EMI-Group/evox/).
## Installation TensorNEAT includes an EvoX Adaptor, which allows TensorNEAT algorithms to run within the EvoX framework. Additionally, TensorNEAT provides a monitor for use with EvoX.
Install `tensorneat` from the GitHub source code:
``` Here is an example of creating an EvoX algorithm and monitor:
pip install git+https://github.com/EMI-Group/tensorneat.git ```python
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 ## Community & Support