sfn缺点
I recently presented at the annual meeting of the society for neuroscience, so I wanted to do a quick post describing my findings.
我最近在神经科学学会年会上作了演讲，所以我想做一篇简短的文章来描述我的发现。
The reinforcement learning literature postulates that we go in and out of exploratory states in order to learn about our environments and maximize the reward we gain in these environments. For example, you might try different foods in order to find the food you most prefer. But, not all novelty seeking behavior results from reward maximization. For example, I often read new books. Maybe reading a new book triggers a reward circuit response, but it certainly doesn’t lead to immediate rewards.
强化学习文献假设我们进入和退出探索状态是为了了解我们的环境并在这些环境中获得最大的回报。 例如，您可以尝试不同的食物以找到最喜欢的食物。 但是，并非所有寻求新颖性的行为都来自奖励最大化。 例如，我经常读书。 也许读一本新书会触发奖励电路React，但肯定不会立即产生奖励。
翻译自: https://www.pybloggers.com/2016/11/sfn-2016-presentation/
sfn缺点