Multi-Modal Mutual Information (MuMMI) Training for Robust Self-Supervised Deep Reinforcement Learning

This refined repository contains the code for the paper Multi-Modal Mutual Information (MuMMI) Training for Robust Self-Supervised Deep Reinforcement Learning (ICRA-21).

Introduction

This work focuses on learning useful and robust deep world models using multiple, possibly unreliable, sensors. We find that current methods do not sufficiently encourage a shared representation between modalities; this can cause poor performance on downstream tasks and over-reliance on specific sensors. This version of the codebase brings improvements on these fronts and aims to further enhance the results produced by the method.

Environment Setup

The code is tested on Ubuntu 16.04, Python 3.7 and CUDA 10.2. Please download the relevant Python packages by running the mentioned commands.

Usage

To run this codebase or baselines on mujoco, follow the steps outlined in the Usage Instructions section.

BibTeX

To cite this work, please use the following citation:

@inproceedings{Chen2021MuMMI,
title={Multi-Modal Mutual Information (MuMMI) Training for Robust Self-Supervised Deep Reinforcement Learning},
author={Kaiqi Chen and Yong Lee and Harold Soh},
year={2021},
booktitle={IEEE International Conference on Robotics and Automation (ICRA)}}

Acknowledgement

This repo contains code that's based on the following repos: Yusufma03/CVRL. Modified and refined by Ruuphoria.

References

[Ma et al., 2020] Xiao Ma, Siwei Chen, David Hsu, Wee Sun Lee: Contrastive Variational Model-Based Reinforcement Learning for Complex Observations. In CoRL, 2020.