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A Pre-Trained Fuzzy Reinforcement Learning Method for the Pursuing Satellite in a One-to-One Game in Space
2020
Sensors
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In order to help the pursuer find its advantaged control policy in a one-to-one game in space, this paper proposes an innovative pre-trained fuzzy reinforcement learning algorithm, which is conducted in ...
Compared with the previous algorithms applied in ground games, this is the first time reinforcement learning has been introduced to help the pursuer in space optimize its control policy. ...
Pre-Trained Fuzzy Reinforcement Learning for the Pursuing Satellite in a One-to-One Game in Space The proposed algorithm is single-looped, which means that for the motions of the pursuing satellite P, ...
doi:10.3390/s20082253
pmid:32316134
fatcat:objhuqk4wnbjjegjjyxc67rmqm
UAV Autonomous Target Search Based on Deep Reinforcement Learning in Complex Disaster Scene
2019
IEEE Access
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Through deep intensive learning and training, the Snake (or self-learning Snake) learns to find the target path autonomously, and the average score on the Snake Game exceeds the average score on human ...
However, most of the existing reinforcement learning is applied in games with only two or three moving directions. ...
ACKNOWLEDGMENT The authors would like to appreciate all anonymous reviewers for their insightful comments and constructive suggestions to polish this article in high quality. ...
doi:10.1109/access.2019.2933002
fatcat:ys4niqndlbhzphwko2way46are
Reinforcement Learning with Variable Fractional Order Approach for MPPT Control of PV Systems for the Real Operating Climatic Condition
2021
International journal of recent technology and engineering
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The model designed in this article combats both the challenges as it is based on reinforcement learning with fractional-order. ...
The application of Deep Q-learning makes the model parametric free and once the model trained can be implanted in a different scenario and run effectively. ...
The introduction of reinforcement learning is done to make the model independent of parametric variations in the design to adapt the environmental effects and also the training is done using a Deep Q-learning ...
doi:10.35940/ijrte.a5631.0510121
fatcat:acqxdyfnbne2flkafbqnnpkutm
Scalable agent alignment via reward modeling: a research direction
[article]
2018
arXiv
pre-print
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One obstacle to applying reinforcement learning algorithms to real-world problems is the lack of suitable reward functions. ...
We outline a high-level research direction to solve the agent alignment problem centered around reward modeling: learning a reward function from interaction with the user and optimizing the learned reward ...
For detailed feedback we are particularly grateful to Paul Christiano, Andreas Stuhlmüller, Ramana Kumar, Laurent Orseau, Edward Grefenstette, Klaus Greff, Shahar Avin, Tegan Maharaj, Victoria Krakovna ...
arXiv:1811.07871v1
fatcat:sbajbquhenh3nmeu3njm3uw5fu
Applications and Challenges of Artificial Intelligence in Space Missions
2021
IEEE Access
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These limitations have necessitated the need to have a concise survey with a wider scope for those interested in the applications and challenges of AI in the space industry, especially those with technical ...
However, a large number of surveys on the applications of AI in space missions can be classified into two categories. ...
However, a satellite can reduce the amount of data transmitted by employing DL for on-board pre-processing. ...
doi:10.1109/access.2021.3132500
fatcat:2n5el5dcqzgdtc3brca5xwxrfu
Autonomous driving: cognitive construction and situation understanding
2019
Science China Information Sciences
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The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring ...
The computing framework of autonomous driving based on a selective attention mechanism and intuitive reasoning discussed in this study can adapt to a more complex, open, and dynamic traffic environment ...
Extending the model trained by deep learning in a simulation environment to a real scene based on transfer learning is one of the methods used to realize intuitive reasoning for autonomous vehicles, which ...
doi:10.1007/s11432-018-9850-9
fatcat:qys3uucz3zgznfou6vgfjerwlq
Deep Learning in Mobile and Wireless Networking: A Survey
2019
IEEE Communications Surveys and Tutorials
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One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. ...
The recent success of deep learning underpins new and powerful tools that tackle problems in this space. ...
The overall framework includes a pre-trained model, Deep Skill Network, which is trained a-priori on various sub-tasks of the game. ...
doi:10.1109/comst.2019.2904897
fatcat:xmmrndjbsfdetpa5ef5e3v4xda
Deep Learning in Mobile and Wireless Networking: A Survey
[article]
2019
arXiv
pre-print
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The recent success of deep learning underpins new and powerful tools that tackle problems in this space. ...
One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications. ...
The overall framework includes a pre-trained model, Deep Skill Network, which is trained a-priori on various sub-tasks of the game. ...
arXiv:1803.04311v3
fatcat:awuvyviarvbr5kd5ilqndpfsde
Sustainable Marine Ecosystems: Deep Learning for Water Quality Assessment and Forecasting
2021
IEEE Access
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Reinforcement learning: there is no predefined dataset. The algorithm is learning how to map situations to actions within a certain environment on their own, so as to maximize a numerical reward. ...
The algorithm is used to effectively train a neural network through a method called chain rule. ...
Sustainable Marine Ecosystems: Deep Learning for Water Quality Assessment and Forecasting EDUARD ANGELATS holds a M.Sc. in Telecommunications Engineering from Technical University of Catalonia (UPC) in ...
doi:10.1109/access.2021.3109216
fatcat:e4mubouhprcm3l2kanxjtrir54
Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency
2021
Applied Sciences
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In addition to elaborating on the principles and applications of the AI-based modeling approaches widely used in building energy use prediction, an evaluation framework is introduced and used for assessing ...
Finally, the paper includes a discussion on the open challenges and future directions of research on the application of AI in smart buildings. ...
Acknowledgments: The authors wish to thank the editor and the reviewers for their contributions on the paper.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app11020763
fatcat:3ipak4rmyba67jdrds6fpyuple
Tackling Climate Change with Machine Learning
[article]
2019
arXiv
pre-print
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We call on the machine learning community to join the global effort against climate change. ...
Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. ...
The authors gratefully acknowledge support from National Science Foundation grant 1803547, the Center for Climate and Energy Decision Making through a cooperative agreement between the National Science ...
arXiv:1906.05433v2
fatcat:ykmqsivkbfcazaz3wl5f7srula
Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review
2019
Journal of Imaging
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On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary ...
Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain. ...
The two networks are trained in a two-player minmax game scheme until the generated data are not distinguishable from the real ones. ...
doi:10.3390/jimaging5050052
pmid:34460490
fatcat:ledlmt42bfdtdhe7tvj2dl2rwm
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities
2018
Journal of Internet Services and Applications
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In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing ...
There are various surveys on ML for specific areas in networking or for specific network technologies. ...
Acknowledgments We thank the anonymous reviewers for their insightful comments and suggestions that helped us improve the quality of the paper. ...
doi:10.1186/s13174-018-0087-2
fatcat:jvwpewceevev3n4keoswqlcacu
Recent advances in mechatronics
1996
Robotics and Autonomous Systems
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The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or ...
Four years has since then passed and in order to discuss the most recent advances, it has been decided to hold another similar conference during 24- ...
The work was supported in part by the Asahi Glass Foundation, and by the Ministry of Education, Science and Culture under Grant-in-Aid for Developmental Scientific Research. ...
doi:10.1016/s0921-8890(96)00039-5
fatcat:l5fd4hwa2rbu3l6f2jucyj2fxy
A Survey of Online Data-Driven Proactive 5G Network Optimisation Using Machine Learning
2020
IEEE Access
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It will examine the state-of-the-art methods currently employed to infer location, social behaviour, and traffic demand through a cloud-edge computing framework; combining them to form the input to proactive ...
The second part of the review focuses on exploiting and integrating the demand knowledge for a range of proactive optimisation techniques, including the key aspects of load balancing, mobile edge caching ...
Q-learning is a reinforcement learning to solve the problem by learning the state-action table from training data. ...
doi:10.1109/access.2020.2975004
fatcat:ccl2trwgkrek5fmkorfwjesq6q
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