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نتیجه جستجو - Deep RL

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 Deep Reinforcement Learning and Its Neuroscientific Implications
یادگیری تقویتی عمیق و پیامدهای عصبی علمی آن-2020
The emergence of powerful artificial intelligence (AI) is defining new research directions in neuroscience. To date, this research has focused largely on deep neural networks trained using supervised learning in tasks such as image classification. However, there is another area of recent AI work that has so far received less attention from neuroscientists but that may have profound neuroscientific implications: deep reinforcement learning (RL). Deep RL offers a comprehensive framework for studying the interplay among learning, representation, and decision making, offering to the brain sciences a new set of research tools and a wide range of novel hypotheses. In the present review, we provide a high-level introduction to deep RL, discuss some of its initial applications to neuroscience, and survey its wider implications for research on brain and behavior, concluding with a list of opportunities for next-stage research.
مقاله انگلیسی
2 Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids
رویکرد یادگیری تقویتی عمیق برای کنترل MPPT سیستم های PV نیمه سایه دار در شبکه های هوشمند-2020
Photovoltaic systems (PV) are having an increased importance in modern smart grids systems. Usually, in order to maximize the energy output of the PV arrays a maximum power point tracking (MPPT) algorithm is used. However, once deployed, weather conditions such as clouds can cause shades in the PV arrays affecting the dynamics of each panel differently. These conditions directly affect the available energy output of the arrays and in turn make the MPPT task extremely difficult. For these reasons, under partial shading conditions, it is necessary to have algorithms that are able to learn and adapt online to the changing state of the system. In this work we propose the use of deep reinforcement learning (DRL) techniques to address the MPPT problem of a PV array under partial shading conditions. We develop a model free RL algorithm to maximize the efficiency in MPPT control. The agent’s policy is parameterized by neural networks, which take the sensory information as input and directly output the control signal. Furthermore, a PV environment under shading conditions was developed in the open source OpenAI Gym platform and is made available in an open repository. Several tests are performed, using the developed simulated environment, to test the robustness of the proposed control strategies to different climate conditions. The obtained results show the feasibility of our proposal with a successful performance with fast responses and stable behaviors. The best results for the presented methodology show that the maximum operating power point achieved has a deviation less than 1% compared to the theoretical maximum power point.
Keywords: MPPT | Deep RL | PV systems | OpenAI Gym
مقاله انگلیسی
3 Design of control framework based on deep reinforcement learning and Monte-Carlo sampling in downstream separation
طراحی چارچوب کنترل مبتنی بر یادگیری تقویت عمیق و نمونه برداری از مونت-کارلو در جداسازی پایین دست-2020
This paper proposes a systematic framework to develop deep reinforcement learning (RL)-based algo- rithms for control system of downstream separation in biopharmaceutical process as follows. First, a sim- ulation model as a digital twin is built and Monte-Carlo sampling generates substantial amounts of sam- ples considering disturbances. Second, the deep RL-based control system is designed and the optimization subject to sample datasets is conducted. The methodology is implemented in a prototype software and relevant codes are shared by Mendeley Data. The proposed model is successfully applied to control the liquid-liquid extraction column for the recovery of fusidic acid as part of downstream processing. The resulting deep RL algorithm provides an operation performance with a better API recovery yield (32 % higher than open loop operation) and lower deviations (23 % lower than open loop operation) against disturbances.
Keywords: Liquid-liquid extraction column | Deep reinforcement learning | Monte-Carlo sampling | Control system | API production | Biopharmaceuticals
مقاله انگلیسی
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