World Models

This repo reproduces the original implementation of World Models. This implementation uses TensorFlow 2.2.

Docker

The easiest way to handle dependencies is with Nvidia-Docker. Follow the instructions below to generate and attach to the container.

docker image build -t wm:1.0 -f docker/Dockerfile.wm .
docker container run -p 8888:8888 --gpus '"device=0"' --detach -it --name wm wm:1.0
docker attach wm

Visualizations

To visualize the environment from the agents perspective or generate synthetic observations use the visualizations jupyter notebook. It can be launched from your container with the following:

jupyter notebook --no-browser --port=8888 --ip=0.0.0.0 --allow-root

Real Frame Sample	Reconstructed Real Frame	Imagined Frame

Ground Truth (CarRacing)	Reconstructed

Ground Truth Environment (DoomTakeCover)	Dream Environment

Reproducing Results From Scratch

These instructions assume a machine with a 64 core cpu and a gpu. If running in the cloud it will likely financially make more sense to run the extraction and controller processes on a cpu machine and the VAE, preprocessing, and RNN tasks on a GPU machine.

DoomTakeCover-v0

CAUTION The doom environment leaves some processes hanging around. In addition to running the doom experiments, the script kills processes including 'vizdoom' in the name (be careful with this if you are not running in a container). To reproduce results for DoomTakeCover-v0 run the following bash script.

bash launch_scripts/wm_doom.bash

CarRacing-v0

To reproduce results for CarRacing-v0 run the following bash script

bash launch_scripts/carracing.bash

Disclaimer

I have not run this for long enough(~45 days wall clock time) to verify that we produce the same results on CarRacing-v0 as the original implementation.

Average return curves comparing the original implementation and ours. The shaded area represents a standard deviation above and below the mean.

For simplicity, the Doom experiment implementation is slightly different than the original

• We do not use weighted cross entropy loss for done predictions
• We train the RNN with sequences that always begin at the start of an episode (as opposed to random subsequences)
• We sample whether the agent dies (as opposed to a deterministic cut-off)