An implementation of Generalized Compressed Network Search for PyTorch models.
This creates a genome per layer, rather than a single one for the entire model as is described in the paper.
There are two optimizers:
- Asynchronous Gene Pool: a master list of genomes, sorted by fitness, is worked on by a pool of agents which draw fit genomes for mutation and evaluation.
- Synchronous Score Swap: each worker maintains a full copy of all genomes and only fitness scores are exchanged.
Install python package with
pip install pytorch-cns
Install redis which is used as the datastore. The examples
expect a redis instance listening on localhost at the default port. You can change
this by passing a JSON dict of
StrictRedis kwargs via the
AI Gym Examples
atari.py: Atari ram-based games
atari_pix.py: Atari pixel-based games
atari_pixrnn_gpa.py: Atari pixel-based games with a recurrent neural network, using the asynchronous gene pool optimizer.
atari_pixrnn_ss.py: Atari pixel-based games with a recurrent neural network, using the synchronous
Install additional requirements:
pip install gym atari_py box2d
To run a pool of workers with default settings simply run the python file
python atari_pix.py). If you make any changes to the hyperparameters
you'll to use the
--clear-store flag which deletes the old gene pool upon start.
--num-agents to customize the number of child processes spawned.
Invoke the example with
python atari_pix.py --render --best to run the simulation
with the fittest genome. This can be done at the same time as the workers are
running to monitor progress.
These do not converge on anything at the moment. Maybe, if you are a real GANimal, you can find the right configuration. The code here is hacked together from the pytorch example repo.
cnsdcgan.py: DCGAN adapted from the PyTorch DCGAN example. Attempts to train
both the discriminator and generator with compressed network search.
vggmse.py: An autoencoder which uses VGG16 to calculate the loss.