Conference item
GradientDICE: rethinking generalized offline estimation of stationary values
- Abstract:
-
We present GradientDICE for estimating the density ratio between the state distribution of the target policy and the sampling distribution in off-policy reinforcement learning. GradientDICE fixes several problems of GenDICE (Zhang et al., 2020), the current state-of-the-art for estimating such density ratios. Namely, the optimization problem in GenDICE is not a convex-concave saddle-point problem once nonlinearity in optimization variable parameterization is introduced to ensure positivity, s...
Expand abstract
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Accepted manuscript, 1.4MB)
-
- Publication website:
- http://proceedings.mlr.press/v119/zhang20r.html
Why is the content I wish to access not available via ORA?
Bibliographic data (the information relating to research outputs) and full-text items (e.g. articles, theses, reports, etc.) arrive in ORA from several different sources. Unfortunately we are not able to make available the full-text for every research output.
Please contact the ORA team (
) if you have queries regarding unavailable content OR if you are aware of a full-text copy we can make available.
) if you have queries regarding unavailable content OR if you are aware of a full-text copy we can make available.
Content may be unavailable for the following four reasons
-
Version unsuitable
-
We have not obtained a suitable full-text for a given research output. See this page for more information.
-
Recently completed
-
Sometimes content is held in ORA but is unavailable for a fixed period of time to comply with the policies and wishes of rights holders.
-
Permissions
-
All content made available in ORA should comply with relevant rights, such as copyright. See this page for more information.
-
Clearance
-
Some thesis volumes scanned as part of the digitisation scheme funded by Dr Leonard Polonsky are currently unavailable due to sensitive material or uncleared third-party copyright content. We are attempting to contact authors whose theses are affected.
Alternative access to the full-text
You may be able to access the full-text directly from the publisher's website using the 'Publisher Copy' link in the 'Links & Downloads' box from a research output's ORA record page. This method may require an institutional or individual subscription to the journal/resource.
Authors
Funding
Bibliographic Details
- Publisher:
- Journal of Machine Learning Research Publisher's website
- Series:
- Proceedings of Machine Learning Research
- Series number:
- 119
- Host title:
- International Conference on Machine Learning, 13-18 July 2020, Virtual
- Publication date:
- 2020-11-21
- Acceptance date:
- 2020-06-01
- Event title:
- 37th International Conference on Machine Learning (ICML 2020)
- Event location:
- Virtual
- Event website:
- https://icml.cc/Conferences/2020
- Event start date:
- 2020-07-12T00:00:00Z
- Event end date:
- 2020-07-18T00:00:00Z
- ISSN:
-
2640-3498
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1118780
- Local pid:
- pubs:1118780
- Deposit date:
- 2020-07-15
Terms of use
- Copyright holder:
- Zhang, s et al.
- Copyright date:
- 2020
- Rights statement:
- © 2020 The Authors.
- Notes:
- This paper was presented at the 37th International Conference on Machine Learning (ICML 2020), 12-18 July 2020. This is the accepted manuscript version of the paper. The final version is available online from PMLR at: http://proceedings.mlr.press/v119/zhang20r.html
If you are the owner of this record, you can report an update to it here: Report update to this record