Hindsight policy gradients
- Submitting institution
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Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 549
- Type
- E - Conference contribution
- DOI
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- Title of conference / published proceedings
- International Conference on Learning Representations
- First page
- 1
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- -
- Year of publication
- 2019
- URL
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- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Simulated robotics videos can be found on http://paulorauber.com/hpg. Conference acceptance rate: 31.4% or 500/1591. An earlier version of this paper is mentioned in a blog post by OpenAI as a promising direction (https://tinyurl.com/y9hrh8pp). OpenAI is one of the largest research groups in AI, and their posts attract significant attention (https://tinyurl.com/ybq4x743). P. Rauber gave a talk at NNAISENSE about this work (Switzerland, 2017, Reference: Rupesh Srivastava, rupesh.srivastava@nnaisense.com, ~10 researchers). Avinash Ummadisingu was hired by well-known Japanese startup Preferred Networks to work in the field after finishing his thesis (Japan, 2019, https://tinyurl.com/y8cw5gov).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -