This is an implementation of NEAT: NeuroEvolution of Augmenting Topologies in Python.
| Environments | Plot (reward vs. generations) | Evolved Neural Network Architecture |
|---|---|---|
pip install numpy scipy matplotlib
conda install pygraphviz
pip install networkxInstallation of OpenAI Gym: [link]
This is a minimal implementation of NEAT. I haven't used any sort of parallel computing tricks like multiprocessing over here. The implementation should be fairly easy to understand.
python run_neat.py --help
python run_neat.py --task=lunar # Run NEAT to learn lunar-lander policyFor any customization:
- Different tasks: Edit
run_neat.py - Hyperparameters: Edit
pyneat/config.py
- Stanley, Kenneth O., and Risto Miikkulainen. "Efficient evolution of neural network topologies." Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600). Vol. 2. IEEE, 2002.
- neat-python: https://github.com/CodeReclaimers/neat-python
Refer the following implementation for additional (advanced) functionalities.
- https://github.com/google/brain-tokyo-workshop/tree/master/WANNRelease/prettyNEAT
- MultiNEAT
- pureples
Please let me know if you come across any bugs in this implementation. Feel free to send any pull requests.