{"id":13906,"date":"2022-10-05T20:13:56","date_gmt":"2022-10-05T14:43:56","guid":{"rendered":"https:\/\/jassweb.com\/solved\/solved-how-do-you-train-a-neural-network-without-an-exact-answer-closed\/"},"modified":"2022-10-05T20:13:56","modified_gmt":"2022-10-05T14:43:56","slug":"solved-how-do-you-train-a-neural-network-without-an-exact-answer-closed","status":"publish","type":"post","link":"https:\/\/jassweb.com\/solved\/solved-how-do-you-train-a-neural-network-without-an-exact-answer-closed\/","title":{"rendered":"[Solved] How do you train a neural network without an exact answer? [closed]"},"content":{"rendered":"

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TLDR; Reinforcement learning<\/p>\n

In general, training agents uses reinforcement learning. It is different than what you explained, because it seems as if you want to define a fitness heuristic<\/em> to tell the agent whether it is doing OK or not, which might be biased. Reinforcement learning also has biases, but they are researchedand studied. A typical bias is a factor to determine how important previous actions are w.r.t. the current action to the current result.<\/p>\n

With reinforcement learning, you only get positive or negative feedback for actions from time to time. You can only learn form those feedback-moments. Unfortunately, this means you only, easily learn ‘winning actions’, ‘leading to winning actions’ are harder. So you need a trick, typically something recursive in your evaluation-function, to make it work. The good news is, researchers have already come up with such tricks. You can start with temporal difference learning or Q-learning. If your model is Neural Network-based, they are trained with gradient descent typically.<\/p>\n<\/p><\/div>\n

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solved How do you train a neural network without an exact answer? [closed] <\/p>\n","protected":false},"excerpt":{"rendered":"

[ad_1] TLDR; Reinforcement learning In general, training agents uses reinforcement learning. It is different than what you explained, because it seems as if you want to define a fitness heuristic to tell the agent whether it is doing OK or not, which might be biased. Reinforcement learning also has biases, but they are researchedand studied. … Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[320],"tags":[3686,2302,2751,1972,349],"class_list":["post-13906","post","type-post","status-publish","format-standard","hentry","category-solved","tag-backpropagation","tag-deep-learning","tag-keras","tag-neural-network","tag-python"],"yoast_head":"\n[Solved] How do you train a neural network without an exact answer? 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