Results 51 to 60 of about 57,740 (314)

Deep Reinforcement Learning That Matters

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress.
Henderson, Peter, Islam, Riashat, Bachman, Philip, Pineau, Joelle, Precup, Doina, Meger, David   +5 more
openaire   +2 more sources

Deep Reinforcement Learning for Swarm Systems [PDF]

Journal of Machine Learning Research, 2018
31 pages, 12 figures, version 3 (published in JMLR Volume 20)
Hüttenrauch, Maximilian, Adrian, Sosic, Neumann, Gerhard   +2 more
openaire   +5 more sources

Deep reinforcement learning for conservation decisions

Methods in Ecology and Evolution, 2022
Abstract Can machine learning help us make better decisions about a changing planet? In this paper, we illustrate and discuss the potential of a promising corner of machine learning known as deep reinforcement learning (RL) to help tackle the most challenging conservation decision problems.
Marcus Lapeyrolerie, Melissa S. Chapman, Kari E. A. Norman, Carl Boettiger   +3 more
openaire   +3 more sources

Exploration in deep reinforcement learning: A survey

Information Fusion, 2022
This paper reviews exploration techniques in deep reinforcement learning. Exploration techniques are of primary importance when solving sparse reward problems. In sparse reward problems, the reward is rare, which means that the agent will not find the reward often by acting randomly.
Pawel Ladosz, Lilian Weng, Minwoo Kim, Hyondong Oh   +3 more
openaire   +4 more sources

Research Progress in Multi-Domain and Cross-Domain AI Management and Control for Intelligent Electric Vehicles

Energies
Recent breakthroughs in artificial intelligence are accelerating the intelligent transformation of vehicles. Vehicle electronic and electrical architectures are converging toward centralized domain controllers.
Dagang Lu, Yu Chen, Yan Sun, Wenxuan Wei, Shilin Ji, Hongshuo Ruan, Fengyan Yi, Chunchun Jia, Donghai Hu, Kunpeng Tang, Song Huang, Jing Wang   +11 more
doaj   +1 more source

Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications

IEEE Access, 2020
Predicting crop yield based on the environmental, soil, water and crop parameters has been a potential research topic. Deep-learning-based models are broadly used to extract significant crop features for prediction. Though these methods could resolve the
Dhivya Elavarasan, P. M. Durairaj Vincent   +1 more
doaj   +1 more source

Photosynthesis under far‐red light—evolutionary adaptations and bioengineering of light‐harvesting complexes

FEBS Letters, EarlyView.
Phototrophs evolved light‐harvesting systems adapted for efficient photon capture in habitats enriched in far‐red radiation. A subset of eukaryotic pigment‐binding proteins can absorb far‐red photons via low‐energy chlorophyll states known as red forms.
Antonello Amelii, Edoardo Andrea Cutolo, Daniele Montepietra, Claudia Battarra, Roberto Caferri, Stefano Capaldi, Zeno Guardini, Luca Dall'Osto, Roberto Bassi   +8 more
wiley   +1 more source

Deep Reinforcement Learning

, 2018
Under review for Morgan & Claypool: Synthesis Lectures in Artificial Intelligence and Machine ...
openaire   +4 more sources

Current trends in single‐cell RNA sequencing applications in diabetes mellitus

FEBS Open Bio, EarlyView.
Single‐cell RNA sequencing is a powerful approach to decipher the cellular and molecular landscape at a single‐cell resolution. The rapid development of this technology has led to a wide range of applications, including the detection of cellular and molecular mechanisms and the identification and introduction of novel potential diagnostic and ...
Seyed Sajjad Zadian, Khodakaram Jahanbin, Shekoofeh Nikooei, Marzieh Rostaminejad, Arezoo Rahimi, Pedram Abdizadeh, Behnam Alipoor   +6 more
wiley   +1 more source

A survey of deep reinforcement learning technologies for intelligent air combat

Hangkong gongcheng jinzhan
Major aviation nations and related research institutions are focusing on exploration and research of key technologies for intelligent air combat. Deep reinforcement learning combines the perceptual ability of deep learning with the decision-making ...
LI Ni, LIAN Yunxiao, ZHOU Pan, XIE Feng, TANG Zhili, ZHOU Haoran, CHEN Jun   +6 more
doaj   +1 more source