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Pytorch implementation of Deep Neuroevolution of World Models

Paper: Risi and Stanley, "Deep Neuroevolution of Recurrent and Discrete World Models" To appear in: Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO 2019). New York, NY: ACM.

https://arxiv.org/abs/1906.08857

Prerequisites

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

Running the program

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:

python main.py

To test a specific genome:

python 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.
  • --discrete : Switching a discrete version of the VAE on or off.
  • --generations : The number of generations used for training.
  • --setting : The setting determining the mutation operator. 0 = Mutate all three modules (VAE, MDN-RNN, C). 1 = Randomly mutate one of those three modules.

Notes

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" -- python main.py

When running with a discrete VAE, the size of the latent vector is increased to 128 from the 32-dimensional version used for the standard VAE.

Authors

  • Sebastian Risi

License

This project is licensed under the MIT License - see the LICENSE.md file for details

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