Brunel University Research Archive(BURA) preserves and enables easy and open access to all
types of digital content. It showcases Brunel's research outputs.
Research contained within BURA is open access, although some publications may be subject
to publisher imposed embargoes. All awarded PhD theses are also archived on BURA.
Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/24394| Title: | Deterministic policy optimization with clipped value expansion and long-horizon planning |
| Authors: | Gao, S Shi, H Wang, F Wang, Z Zhang, S Li, Y Sun, Y |
| Keywords: | model-based reinforcement learning;policy gradient;sample efficiency;planning;imitation learning |
| Issue Date: | 16-Feb-2022 |
| Publisher: | Elsevier |
| Citation: | Gao, S. et al. (2022) ‘Deterministic policy optimization with clipped value expansion and long-horizon planning’, Neurocomputing, 483, pp. 299 - 310. doi:10.1016/j.neucom.2022.02.022. |
| URI: | https://bura.brunel.ac.uk/handle/2438/24394 |
| DOI: | https://doi.org/10.1016/j.neucom.2022.02.022 |
| ISSN: | 0925-2312 |
| Appears in Collections: | Dept of Computer Science Embargoed Research Papers |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | Embargoed until 16 Feb 2024 | 2.84 MB | Adobe PDF | View/Open |
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.