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/}
}