NeurIPS 2019poster31 citations
Multi-View Reinforcement Learning
Minne Li, Lisheng Wu, Jun WANG, Haitham Bou Ammar
Abstract
This paper is concerned with multi-view reinforcement learning (MVRL), which allows for decision making when agents share common dynamics but adhere to different observation models. We define the MVRL framework by extending partially observable Markov decision processes (POMDPs) to support more than one observation model and propose two solution methods through observation augmentation and cross-view policy transfer. We empirically evaluate our method and demonstrate its effectiveness in a variety of environments. Specifically, we show reductions in sample complexities and computational time for acquiring policies that handle multi-view environments.
BibTeX
@inproceedings{NEURIPS2019_677e0972,
author = {Li, Minne and Wu, Lisheng and WANG, Jun and Bou Ammar, Haitham},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Multi-View Reinforcement Learning},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/677e09724f0e2df9b6c000b75b5da10d-Paper.pdf},
volume = {32},
year = {2019}
}