An Invitation to Deep Reinforcement Learning

An Invitation to Deep Reinforcement Learning

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Training a deep neural network to maximize a target objective has become the standard recipe for successful machine learning over the last decade. These networks can be optimized with supervised learning if the target objective is differentiable. However, this is not the case for many interesting problems. Common objectives like intersection over union (IoU) and bilingual evaluation understudy (BLEU) scores or rewards cannot be optimized with supervised learning. A common workaround is to define differentiable surrogate losses, leading to suboptimal solutions with respect to the actual objecti...