Pure Python implementation of NEAT. Although this project is heavily inspired by the official NEAT Python implementation(NEAT-Python), it's different in that it's not intended to be able to graft NEAT to other projects, but to show intuitively how NEAT works. Therefore, much of the code is designed to help code readers intuitively understand.
You need following library:
> sudo apt install swig libopenmpi-dev # for box2d-py & mpi4py
We recommand you to install in anaconda virtual environment to avoid any dependency issues.
> git clone https://github.com/jinPrelude/pyNeat.git
> conda create -n pyneat python=3.8
> conda activate pyneat
> cd pyNeat
> pip install -r requirements.txt
You can change the training settings by editing the config file. For instance, you can change NEAT algorithm's crossover ratio for CartPole training simply by changing 'crossover_ratio' variable in cartpole config file :
# conf/neat/cartpole.yaml
...
strategy:
name : neat
offspring_num: 300
crossover_ratio: 0.75
...
Then, you can use the flag to run training with the desired config settings.
# training CartPole(POMDP settings by default).
> python run_es.py --cfg-path conf/neat/cartpole.yaml
# training BiedalWalker.
> python run_es.py --cfg-path conf/neat/bipedalwalker.yamlOther running options can be found by running python run_es.py -h :
optional arguments:
-h, --help show this help message and exit
--cfg-path CFG_PATH config file to run.
--seed SEED random seed.
--n-workers N_WORKERS
--generation-num GENERATION_NUM
max number of generation iteration.
--eval-ep-num EVAL_EP_NUM
number of model evaluaion per iteration.
--log wandb log
--save-model-period SAVE_MODEL_PERIOD
save model for every n iteration.
You need wandb account before using logging function. Using --log flag, you can use advanced training logging without any charge. Using --log, You can check the current training progress for every steps, morphology of the Neat network and the test episode result as a gif for every --save-model-period.
