Conference item
Stable opponent shaping in differentiable games
- Abstract:
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A growing number of learning methods are actually differentiable games whose players optimise multiple, interdependent objectives in parallel – from GANs and intrinsic curiosity to multi-agent RL. Opponent shaping is a powerful approach to improve learning dynamics in these games, accounting for player influence on others’ updates. Learning with Opponent-Learning Awareness (LOLA) is a recent algorithm that exploits this response and leads to cooperation in settings like the Iterated Prisoner’...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Bibliographic Details
- Publisher:
- OpenReview Publisher's website
- Host title:
- ICLR 2019: Proceedings of the Seventh International Conference on Learning Representations
- Journal:
- 2019 International Conference on Learning Representations Journal website
- Publication date:
- 2019-01-27
- Acceptance date:
- 2019-02-22
Item Description
- Keywords:
- Pubs id:
-
pubs:975272
- UUID:
-
uuid:8d732709-113f-4304-ad0b-ecc9e8d1ea03
- Local pid:
- pubs:975272
- Source identifiers:
-
975272
- Deposit date:
- 2019-02-22
Terms of use
- Copyright holder:
- Letcher et al
- Copyright date:
- 2019
- Notes:
- This paper was presented at the Seventh International Conference on Learning Representations (ICLR), 6-9 May 2019, New Orleans, USA.
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