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pro vyhledávání: '"Jordan, Scott M."'
Autor:
Kostas, James E., Jordan, Scott M., Chandak, Yash, Theocharous, Georgios, Gupta, Dhawal, White, Martha, da Silva, Bruno Castro, Thomas, Philip S.
Coagent networks for reinforcement learning (RL) [Thomas and Barto, 2011] provide a powerful and flexible framework for deriving principled learning rules for arbitrary stochastic neural networks. The coagent framework offers an alternative to backpr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c1052ad7e823707ca3dcad2409f0b8f0
http://arxiv.org/abs/2305.09838
http://arxiv.org/abs/2305.09838
Many real-world sequential decision-making problems involve critical systems with financial risks and human-life risks. While several works in the past have proposed methods that are safe for deployment, they assume that the underlying problem is sta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cbbe2b153e084a687d46c40beb982700