@inproceedings{
huang2024leveraging,
title={Leveraging Separated World Model for Exploration in Visually Distracted Environments},
author={Kaichen Huang and Shenghua Wan and Minghao Shao and Hai-Hang Sun and Le Gan and Shuai Feng and De-Chuan Zhan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Osh7u2E1kC}
}
Model-based unsupervised reinforcement learning (URL) has gained prominence for reducing environment interactions and learning general skills using intrinsic rewards. However, distractors in observations can severely affect intrinsic reward estimation, leading to a biased exploration process, especially in environments with visual inputs like images or videos. To address this challenge, we propose a bi-level optimization framework named Separation-assisted eXplorer (SeeX). In the inner optimization, SeeX trains a separated world model to extract exogenous and endogenous information, minimizing uncertainty to ensure task relevance. In the outer optimization, it learns a policy on imaginary trajectories generated within the endogenous state space to maximize task-relevant uncertainty. Evaluations on multiple locomotion and manipulation tasks demonstrate SeeX's effectiveness.
Leveraging Separated World Model for Exploration in Visually Distracted Environments
Kaichen Huang Shenghua Wan Minghao Shao Hai-Hang Sun Le Gan Shuai Feng De-Chuan Zhan