Reinforcement Learning for Electric Power System Decision and Control: Past Considerations and Perspectives
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In this paper, the authors review past and very recent research considerations in using reinforcement learning (RL) to solve electric power system decision and control problems, and analyse the perspectives of RL approaches in light of the emergence of new generation, communications, and instrumentation technologies currently in use, or available for future use, in power systems.About:
This article is published in IFAC-PapersOnLine.The article was published on 2017-07-01 and is currently open access. It has received 170 citations till now. The article focuses on the topics: Reinforcement learning.read more
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Deep Reinforcement Learning: An Overview
TL;DR: This work discusses core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration, and important mechanisms for RL, including attention and memory, unsupervised learning, transfer learning, multi-agent RL, hierarchical RL, and learning to learn.
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Tackling Climate Change with Machine Learning
David Rolnick,Priya L. Donti,Lynn H. Kaack,K. Kochanski,Alexandre Lacoste,Kris Sankaran,Andrew S. Ross,Nikola Milojevic-Dupont,Natasha Jaques,Anna Waldman-Brown,Alexandra Luccioni,Tegan Maharaj,Evan D. Sherwin,S. Karthik Mukkavilli,Konrad P. Kording,Carla P. Gomes,Andrew Y. Ng,Demis Hassabis,John Platt,Felix Creutzig,Jennifer Chayes,Yoshua Bengio +21 more
TL;DR: From smart grids to disaster management, high impact problems where existing gaps can be filled by ML are identified, in collaboration with other fields, to join the global effort against climate change.
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A Study on Overfitting in Deep Reinforcement Learning
TL;DR: This paper conducts a systematic study of standard RL agents and finds that they could overfit in various ways and calls for more principled and careful evaluation protocols in RL.
Journal ArticleDOI
An Overview of Artificial Intelligence Applications for Power Electronics
TL;DR: The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration.
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Deep reinforcement learning
Yuxi Li,Felix Wittstock +1 more
TL;DR: This work discusses deep reinforcement learning in an overview style, focusing on contemporary work, and in historical contexts, with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources.
References
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Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
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TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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Mastering the game of Go with deep neural networks and tree search
David Silver,Aja Huang,Chris J. Maddison,Arthur Guez,Laurent Sifre,George van den Driessche,Julian Schrittwieser,Ioannis Antonoglou,Veda Panneershelvam,Marc Lanctot,Sander Dieleman,Dominik Grewe,John Nham,Nal Kalchbrenner,Ilya Sutskever,Timothy P. Lillicrap,Madeleine Leach,Koray Kavukcuoglu,Thore Graepel,Demis Hassabis +19 more
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