Heuristic action execution for energy efficient charge-sustaining control of connected hybrid vehicles with model-free double Q-learning

We introduce a novel three stepwise model of adaptive e-learning using multiple learner characteristics. We design a model of a learner attributes enlisting the study domain, summary details of the student and the requirements of the student. We include the theories of learning style to categorize and identify specific individuals so as to improve their experience on the online learning platform and apply it in the model. The affective state extraction model which extracts learner emotions from text inputs during the platform interactions. We finally pass the system extracted information the adaptivity domain which uses the off-policy Q-learning model free algorithm (Jang et al., 2019) to structure the learning path into tutorials, lectures and workshops depending on predefined constraints of learning. Simulated results show better adaptivity incases of multiple characteristics as opposed to single learner characteristics. Further research to include more than three characteristics as in this research.

Download Full-text

Energy-Efficient Resource Allocation in Cognitive Radio Networks Under Cooperative Multi-Agent Model-Free Reinforcement Learning Schemes

IEEE Transactions on Network and Service Management ◽

10.1109/tnsm.2020.3000274 ◽

2020 ◽

Vol 17 (3) ◽

pp. 1337-1348 ◽

Cited By ~ 3

Author(s):

Amandeep Kaur ◽

Krishan Kumar

Keyword(s):

Resource Allocation ◽

Cognitive Radio ◽

Energy Efficient ◽

Radio Networks ◽

Agent Model ◽

Model Free ◽

Efficient Resource ◽

Multi Agent ◽

Energy Efficient Resource Allocation ◽

Learning Schemes

Download Full-text

Q-Learning Based Social Community-Aware Energy Efficient Cooperative Caching in 5G Networks

2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN) ◽

10.1109/icufn.2019.8806093 ◽

2019 ◽

Cited By ~ 1

Author(s):

Han Yeo Reum Im ◽

Kyi Thar ◽

Choong Seon Hong

Keyword(s):

Energy Efficient ◽

Cooperative Caching ◽

5G Networks ◽

Social Community ◽

Q Learning

Download Full-text

Reinforcement Learning (RL)-Based Energy Efficient Resource Allocation for Energy Harvesting-Powered Wireless Body Area Network

Sensors ◽

10.3390/s20010044 ◽

2019 ◽

Vol 20 (1) ◽

pp. 44 ◽

Cited By ~ 6

Author(s):

Yi-Han Xu ◽

Jing-Wei Xie ◽

Yang-Gang Zhang ◽

Min Hua ◽

Wen Zhou

Keyword(s):

Resource Allocation ◽

Energy Efficiency ◽

Energy Harvesting ◽

Energy Efficient ◽

Area Network ◽

Allocation Strategy ◽

Body Area ◽

Q Learning ◽

Efficient Resource ◽

Energy Efficient Resource Allocation

Wireless body area networks (WBANs) have attracted great attention from both industry and academia as a promising technology for continuous monitoring of physiological signals of the human body. As the sensors in WBANs are typically battery-driven and inconvenient to recharge, an energy efficient resource allocation scheme is essential to prolong the lifetime of the networks, while guaranteeing the rigid requirements of quality of service (QoS) of the WBANs in nature. As a possible alternative solution to address the energy efficiency problem, energy harvesting (EH) technology with the capability of harvesting energy from ambient sources can potentially reduce the dependence on the battery supply. Consequently, in this paper, we investigate the resource allocation problem for EH-powered WBANs (EH-WBANs). Our goal is to maximize the energy efficiency of the EH-WBANs with the joint consideration of transmission mode, relay selection, allocated time slot, transmission power, and the energy constraint of each sensor. In view of the characteristic of the EH-WBANs, we formulate the energy efficiency problem as a discrete-time and finite-state Markov decision process (DFMDP), in which allocation strategy decisions are made by a hub that does not have complete and global network information. Owing to the complexity of the problem, we propose a modified Q-learning (QL) algorithm to obtain the optimal allocation strategy. The numerical results validate the effectiveness of the proposed scheme as well as the low computation complexity of the proposed modified Q-learning (QL) algorithm.

Download Full-text

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.787.843 ◽

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.

Download Full-text