Model-Free Perception-Based Control via Q-Learning with an Application to Heat-Seeking Missile Guidance

2021 ◽  
Author(s):  
Wade S. Kovalik ◽  
Lijing Zhai ◽  
Kyriakos G. Vamvoudakis
Keyword(s):  
Missile Guidance ◽  
Q Learning ◽  
Model Free

scholarly journals Reinforcement Learning Approach for Adaptive e-Learning Based on Multiple Learner Characteristics

2021 ◽  
Vol 4 (2) ◽  
pp. 55-76
Author(s):  
Dan Oyuga Anne ◽  
Elizaphan Maina
Keyword(s):  
Learning Style ◽  
Affective State ◽  
Learner Characteristics ◽  
Q Learning ◽  
Model Free ◽  
Learning Platform ◽  
Learning Path ◽  
Multiple Characteristics ◽  
E Learning ◽  
Extraction Model

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.

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.

scholarly journals Gaussian Process Based Model-free Control with Q-Learning

2019 ◽  
Vol 52 (11) ◽  
pp. 236-243
Author(s):  
Jan Hauser ◽  
Daniel Pachner ◽  
Vladimír Havlena
Keyword(s):  
Gaussian Process ◽  
Q Learning ◽  
Model Free ◽  
Model Free Control

scholarly journals Model Free Optimal Control of Two Whole Buildings using Deep Q-Learning

2020 ◽  
Author(s):  
Ki Uhn Ahn ◽  
Jae Min Kim ◽  
Youngsub Kim ◽  
Cheol Soo Park ◽  
Kwang Woo Kim
Keyword(s):  
Optimal Control ◽  
Q Learning ◽  
Model Free

Computational Reinforcement Learning

2015 ◽  
Author(s):  
Todd M. Gureckis ◽  
Bradley C. Love
Keyword(s):  
Reinforcement Learning ◽  
Instrumental Learning ◽  
Learning Behavior ◽  
Q Learning ◽  
Mathematical Ideas ◽  
Model Free ◽  
Versus Model ◽  
Open Questions ◽  
History Of ◽  
Exploration Exploitation

Reinforcement learning (RL) refers to the scientific study of how animals and machines adapt their behavior in order to maximize reward. The history of RL research can be traced to early work in psychology on instrumental learning behavior. However, the modern field of RL is a highly interdisciplinary area that lies that the intersection of ideas in computer science, machine learning, psychology, and neuroscience. This chapter summarizes the key mathematical ideas underlying this field including the exploration/exploitation dilemma, temporal-difference (TD) learning, Q-learning, and model-based versus model-free learning. In addition, a broad survey of open questions in psychology and neuroscience are reviewed.

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