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Beyond Bayes-optimality: meta-learning what you know you don't know. (arXiv:2209.15618v1 [cs.AI])
Oct. 3, 2022, 1:12 a.m. | Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Tim Genewein, Elliot Catt, Kevin Li, Anian Ruoss, Chris Cundy, Joel Veness, Jane Wang, M
cs.LG updates on arXiv.org arxiv.org
Meta-training agents with memory has been shown to culminate in Bayes-optimal
agents, which casts Bayes-optimality as the implicit solution to a numerical
optimization problem rather than an explicit modeling assumption. Bayes-optimal
agents are risk-neutral, since they solely attune to the expected return, and
ambiguity-neutral, since they act in new situations as if the uncertainty were
known. This is in contrast to risk-sensitive agents, which additionally exploit
the higher-order moments of the return, and ambiguity-sensitive agents, which
act differently when recognizing …
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