Bias Variance

کنترل زیر شاخه ای از رشته برق است که در آن می توان تصمیم گیری داشت. برای مثال یک ربات چگونه فوتبال بازی کند یا چگونه راه برود. با پیشرفت یادگیری عمیق و یادگیری تقویتی و ادغام این دو، شاهد این هستیم که کارهایی که تا قبل از این دانش پژوهان برق رویشان کار می کردند، به سمت کامیونیتی هوش مصنوعی آماده است. یکی از این زمینه ها رباتیک است. در ادامه مقاله ای مروری از یادگیری عمیق و تقویتی در رباتیک را ارجاع می دهیم که اخیرا چاپ شده است.

A survey on deep learning and deep reinforcement learning in robotics with a tutorial on deep reinforcement learning

This article is about deep learning (DL) and deep reinforcement learning (DRL) works applied to robotics. Both tools have been shown to be successful in delivering data-driven solutions for robotics tasks, as well as providing a natural way to develop an end-to-end pipeline from the robot’s sensing to its actuation, passing through the generation of a policy to perform the given task. These frameworks have been proven to be able to deal with real-world complications such as noise in sensing, imprecise actuation, variability in the scenarios where the robot is being deployed, among others. Following that vein, and given the growing interest in DL and DRL, the present work starts by providing a brief tutorial on deep reinforcement learning, where the goal is to understand the main concepts and approaches followed in the field. Later, the article describes the main, recent, and most promising approaches of DL and DRL in robotics, with sufficient technical detail to understand the core of the works and to motivate interested readers to initiate their own research in the area. Then, to provide a comparative analysis, we present several taxonomies in which the references can be classified, according to high-level features, the task that the work addresses, the type of system, and the learning techniques used in the work. We conclude by presenting promising research directions in both DL and DRL.

#یادگیری_عمیق_تقویتی #یادگیری_عمیق #رباتیک #معرفی_مقاله #کنترل

🌳 پشتیبانی | 🌺 کانال | 🌴 سایت

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A survey on deep learning and deep reinforcement learning in robotics with a tutorial on deep reinforcement learning

Intelligent Service Robotics - This article is about deep learning (DL) and deep reinforcement learning (DRL) works applied to robotics. Both tools have been shown to be successful in delivering...

www.tgoop.com/biasvariance_ir/166

218 viewsBias Variance, edited  Nov 22, 2021 at 10:48

کنترل زیر شاخه ای از رشته برق است که در آن می توان تصمیم گیری داشت. برای مثال یک ربات چگونه فوتبال بازی کند یا چگونه راه برود. با پیشرفت یادگیری عمیق و یادگیری تقویتی و ادغام این دو، شاهد این هستیم که کارهایی که تا قبل از این دانش پژوهان برق رویشان کار می کردند، به سمت کامیونیتی هوش مصنوعی آماده است. یکی از این زمینه ها رباتیک است. در ادامه مقاله ای مروری از یادگیری عمیق و تقویتی در رباتیک را ارجاع می دهیم که اخیرا چاپ شده است.

A survey on deep learning and deep reinforcement learning in robotics with a tutorial on deep reinforcement learning

This article is about deep learning (DL) and deep reinforcement learning (DRL) works applied to robotics. Both tools have been shown to be successful in delivering data-driven solutions for robotics tasks, as well as providing a natural way to develop an end-to-end pipeline from the robot’s sensing to its actuation, passing through the generation of a policy to perform the given task. These frameworks have been proven to be able to deal with real-world complications such as noise in sensing, imprecise actuation, variability in the scenarios where the robot is being deployed, among others. Following that vein, and given the growing interest in DL and DRL, the present work starts by providing a brief tutorial on deep reinforcement learning, where the goal is to understand the main concepts and approaches followed in the field. Later, the article describes the main, recent, and most promising approaches of DL and DRL in robotics, with sufficient technical detail to understand the core of the works and to motivate interested readers to initiate their own research in the area. Then, to provide a comparative analysis, we present several taxonomies in which the references can be classified, according to high-level features, the task that the work addresses, the type of system, and the learning techniques used in the work. We conclude by presenting promising research directions in both DL and DRL.

#یادگیری_عمیق_تقویتی #یادگیری_عمیق #رباتیک #معرفی_مقاله #کنترل

🌳 پشتیبانی | 🌺 کانال | 🌴 سایت

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