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Dec 16, 2020 at 21:16 comment added nbro If you're not satisfied with that answer, eventually, you could ask a similar question but make sure to provide the context and say why you're not satisfied with that answer.
Dec 16, 2020 at 20:57 vote accept Daniel B.
Dec 16, 2020 at 20:56 history edited Daniel B. CC BY-SA 4.0
deleted 689 characters in body
Dec 16, 2020 at 20:54 comment added Daniel B. Never mind. I think this question contains actually the answer to my edited question. I only hope that it is correct because this suspected answer is phrased as part of a question. But it sounds like a reasonable approach. So sorry for the confusion & I will 'revert' the edit.
Dec 16, 2020 at 20:44 comment added Daniel B. I think these answers I am searching for belong fundamentally together since only knowing what to predict does not make sense in absence of the knowledge about how to get to that prediction eventually. And for getting there, the surrogate loss needs to be considered as well since otherwise you don't have any way to properly train the model (in spite of knowing what it shall predict). And just for the context: $r_t(\theta)$ is an important part of the surrogate loss. But anyway. I sort of see your point, so let's make it a separate question then.
Dec 16, 2020 at 19:35 comment added nbro If you have a new question, you should ask it in a different post, even though it's related to the current question (I actually don't know), because that may invalidate the existing answers.
Dec 16, 2020 at 17:58 history edited Daniel B. CC BY-SA 4.0
added 691 characters in body
Dec 13, 2020 at 13:28 vote accept Daniel B.
Dec 16, 2020 at 17:58
Dec 12, 2020 at 12:03 history edited nbro CC BY-SA 4.0
link to PPO paper added + title
Dec 12, 2020 at 6:22 answer added kaiwenw timeline score: 3
Dec 12, 2020 at 1:42 history asked Daniel B. CC BY-SA 4.0