Li, M;
Wu, L;
Ammar, HB;
Wang, J;
(2019)
Multi-View Reinforcement Learning.
In: Wallach, H and Larochelle, H and Beygelzimer, A and d'Alche-Buc, F and Fox, E and Garnett, R, (eds.)
Advances in Neural Information Processing Systems 32 (NIPS 2019).
Neural Information Processing Systems (NIPS): Vancouver, Canada.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Multi-View Reinforcement Learning |
| Event: | 33rd Conference on Neural Information Processing Systems (NeurIPS) |
| Location: | Vancouver, CANADA |
| Dates: | 08 December 2019 - 14 December 2019 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://papers.nips.cc/book/advances-in-neural-inf... |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10110448 |
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