What if we Reduce the Memory of an Artificial Doom Player?

Online exploration of memory reduction strategies of a DRL agent trained to solve a navigation task on ViZDoom.

Authors: Théo Jaunet, Romain Vuillemot and Christian Wolf.

Live Demo

This tool is accessible using the following link: https://theo-jaunet.github.io/MemoryReduction/. (designed to work on desktop with google chrome)

Running it Locally

To run this interface locally, download or clone this repository

git clone https://github.com/Theo-Jaunet/MemoryReduction.git

Open the downloaded directory and start any server. For demonstration sake we used: SimpleHTTPServer

python -m SimpleHTTPServer 8000

Once the server is launched, you should be able to access the explorable at: http://localhost:8000/.

The DRL Model Used & Scenario

For more information on this task and model, please check Edward Beeching's github repo.

How to Cite

If you find this work useful, please consider using the follwing citing template:

@inproceedings{Jaunet:2019,
  author = {Theo Jaunet, Romain Vuillemot, Christian Wolf},
	title = {What if we Reduce the Memory of an Artificial Doom Player?},
	journal = {Proceedings of the Workshop on Visualization for AI explainability (VISxAI)},
	year = {2019},
	editors = {Mennatallah El-Assady, Duen Horng (Polo) Chau, Fred Hohman, Adam Perer, Hendrik Strobelt, Fernanda Viégas}
	url = {https://theo-jaunet.github.io/MemoryReduction/}
}