scholarly journals Biomimetic ultra-broadband perfect absorbers optimised with reinforcement learning

2020 ◽  
Vol 22 (4) ◽  
pp. 2337-2342 ◽  
Author(s):  
Trevon Badloe ◽  
Inki Kim ◽  
Junsuk Rho
Keyword(s):  
Reinforcement Learning ◽  
Transition Metals ◽  
Transfer Learning ◽  
Learning Method ◽  
Q Learning ◽  
Perfect Absorbers

The reinforcement learning method of double deep-Q learning is used to design moth-eye structure-based ultra-broadband perfect absorbers with a variety of transition metals, using transfer learning to share knowledge between different environments.

Energy Optimization of Solar Micro-Grid Using Multi Agent Reinforcement Learning

2015 ◽  
Vol 787 ◽  
pp. 843-847
Author(s):  
Leo Raju ◽  
R.S. Milton ◽  
S. Sakthiyanandan
Keyword(s):  
Reinforcement Learning ◽  
Energy Savings ◽  
Learning Method ◽  
Solar Pv ◽  
Q Learning ◽  
Pv Systems ◽  
Model Free ◽  
Individual Unit ◽  
Multi Agent ◽  
Micro Grid

In this paper, two solar Photovoltaic (PV) systems are considered; one in the department with capacity of 100 kW and the other in the hostel with capacity of 200 kW. Each one has battery and load. The capital cost and energy savings by conventional methods are compared and it is proved that the energy dependency from grid is reduced in solar micro-grid element, operating in distributed environment. In the smart grid frame work, the grid energy consumption is further reduced by optimal scheduling of the battery, using Reinforcement Learning. Individual unit optimization is done by a model free reinforcement learning method, called Q-Learning and it is compared with distributed operations of solar micro-grid using a Multi Agent Reinforcement Learning method, called Joint Q-Learning. The energy planning is designed according to the prediction of solar PV energy production and observed load pattern of department and the hostel. A simulation model was developed using Python programming.

Multi-Agent Reinforcement Learning Based on K-Means Clustering in Multi-Robot Cooperative Systems

2011 ◽  
Vol 216 ◽  
pp. 75-80 ◽  
Author(s):  
Chang An Liu ◽  
Fei Liu ◽  
Chun Yang Liu ◽  
Hua Wu
Keyword(s):  
Reinforcement Learning ◽  
State Space ◽  
Experimental Results ◽  
Learning Ability ◽  
Learning Method ◽  
Q Learning ◽  
State Space Explosion ◽  
Multi Agent ◽  
Robot Cooperation ◽  
Multi Robot

To solve the curse of dimensionality problem in multi-agent reinforcement learning, a learning method based on k-means is presented in this paper. In this method, the environmental state is represented as key state factors. The state space explosion is avoided by classifying states into different clusters using k-means. The learning rate is improved by assigning different states to existent clusters, as well as corresponding strategy. Compared to traditional Q-learning, our experimental results of the multi-robot cooperation show that our scheme improves the team learning ability efficiently. Meanwhile, the cooperation efficiency can be enhanced successfully.

Transfer Learning Method based on Spreading Activation Model for Reinforcement Learning

2018 ◽  
Vol 2018 (0) ◽  
pp. 1A1-C14
Author(s):  
Yusaku TAKAKUWA ◽  
Hitoshi KONO ◽  
Wen WEN ◽  
Akiya KAMIMURA ◽  
Kohji TOMITA ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Transfer Learning ◽  
Spreading Activation ◽  
Learning Method ◽  
Activation Model

A novel multi-step Q-learning method to improve data efficiency for deep reinforcement learning

2019 ◽  
Vol 175 ◽  
pp. 107-117 ◽  
Author(s):  
Yinlong Yuan ◽  
Zhu Liang Yu ◽  
Zhenghui Gu ◽  
Yao Yeboah ◽  
Wu Wei ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning Method ◽  
Q Learning ◽  
Data Efficiency

Transfer Learning Method in Reinforcement Learning-based Traffic Signal Control

2021 ◽  
Author(s):  
Zhenyu Mao ◽  
Jialong Li ◽  
Nianzhao Zheng ◽  
Kenji Tei ◽  
Shinichi Honiden
Keyword(s):  
Reinforcement Learning ◽  
Transfer Learning ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Learning Method

scholarly journals Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems

2019 ◽  
Vol 9 (23) ◽  
pp. 5173 ◽  
Author(s):  
Wu ◽  
Wang ◽  
Li ◽  
Zhang ◽  
Peng
Keyword(s):  
Reinforcement Learning ◽  
Information Acquisition ◽  
Radar System ◽  
Learning Method ◽  
Q Learning ◽  
Radar Systems ◽  
Aerial Vehicle ◽  
Simulation Results ◽  
Joint Programming ◽  
Radar Information

The networked unmanned aerial vehicle (UAV) radar system may exploit inter-UAV cooperation for enhancing information acquisition capabilities, meanwhile its inter-UAV communications may be interfered with by external jammers. This paper is devoted to quantifying and optimizing the anti-jamming performance of networked UAV radar systems in adversarial electromagnetic environments. Firstly, instead of using the conventional metric of signal-to-interference ratio (SIR), this paper explores use of the theory of radar information representation as the basis of evaluating the information acquisition capabilities of the networked UAV radar systems. Secondly, this paper proposes a modified Q-Learning method based on double greedy algorithm to optimize the anti-jamming performance of the networked UAV radar systems, through joint programming in the frequency-motion-antenna domain. Simulation results prove the effectiveness of the algorithm in two different networking scenarios.

A Plant Control Technology Using Reinforcement Learning Method with Automatic Reward Adjustment

2009 ◽  
Vol 129 (7) ◽  
pp. 1253-1263
Author(s):  
Toru Eguchi ◽  
Takaaki Sekiai ◽  
Akihiro Yamada ◽  
Satoru Shimizu ◽  
Masayuki Fukai
Keyword(s):  
Reinforcement Learning ◽  
Control Technology ◽  
Learning Method ◽  
Plant Control
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