- Abstract:
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Policy gradient methods ignore the potential value of adjusting environment variables: unobservable state features that are randomly determined by the environment in a physical setting, but are controllable in a simulator. This can lead to slow learning, or convergence to suboptimal policies, if the environment variable has a large impact on the transition dynamics. In this paper, we present fingerprint policy optimisation (FPO), which finds a policy that is optimal in expectation across the ...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Publisher's Version
- Files:
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(pdf, 144.1kb)
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(pdf, 608.1kb)
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- Publisher:
- Journal of Machine Learning Research Publisher's website
- Volume:
- 97
- Pages:
- 5082-5091
- Publication date:
- 2019-06-11
- Acceptance date:
- 2019-05-14
- ISSN:
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2640-3498
- Pubs id:
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pubs:998018
- URN:
-
uri:0bd8f1b9-236f-4348-90a8-8bf5fbd77d85
- UUID:
-
uuid:0bd8f1b9-236f-4348-90a8-8bf5fbd77d85
- Local pid:
- pubs:998018
- Copyright holder:
- Paul et al.
- Copyright date:
- 2019
- Notes:
- © The Author(s) 2019. This paper was presented at the 36th International Conference on Machine Learning (ICML 2019), Long Beach, California, USA, June 2019. The final published version and supplementary materials are available online from Proceedings of Machine Learning Research at: http://proceedings.mlr.press/v97/paul19a.html
Conference item
Fingerprint policy optimisation for robust reinforcement learning
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