Paper: Risi and Stanley, "Deep Innovation Protection: Confronting the Credit Assignment Problem in Training Heterogeneous Neural Architectures "" Proceedings of the Thirty-Fith AAAI Conference on Artificial Intelligence (AAAI-2021)

https://arxiv.org/abs/2001.01683

The code is partly based on the PyTorch implementation of "World Models" (https://github.com/ctallec/world-models).

Code requieres Python3 and PyTorch (https://pytorch.org). The rest of the requirements are included in the requirements file, to install them:

pip3 install -r requirements.txt

The world model is composed of three different components:

1. A Variational Auto-Encoder (VAE)
2. A Mixture-Density Recurrent Network (MDN-RNN)
3. A linear Controller (C), which takes both the latent encoding and the hidden state of the MDN-RNN as input and outputs the agents action

In contrast to the original world model, all three components are trained end-to-end through evolution. To run training:

python3 main.py

To test a specific genome:

python3 main.py --test best_1_1_G2.p

Additional arguments for the training script are:

• --folder : The directory to store the training results.
• --pop-size : The population size.
• --threads : The number of threads used for training or testing.
• --generations : The number of generations used for training.
• --inno : 0 = Innoviation protection disabled. 1 = Innovation protection enabled.

When running on a headless server, you will need to use xvfb-run to launch the controller training script. For instance,

xvfb-run -a -s "-screen 0 1400x900x24 +extension RANDR" -- python3 main.py

• Sebastian Risi