Model-Free Algorithms for Containment Control of Saturated Discrete-Time Multiagent Systems via Q-Learning Method

2020 ◽  
pp. 1-9
Author(s):  
Mingkang Long ◽  
Housheng Su ◽  
Zhigang Zeng
Keyword(s):  
Multiagent Systems ◽  
Discrete Time ◽  
Learning Method ◽  
Containment Control ◽  
Q Learning ◽  
Model Free

scholarly journals Improved Q-Learning Method for Linear Discrete-Time Systems

Processes ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 368
Author(s):  
Jian Chen ◽  
Jinhua Wang ◽  
Jie Huang
Keyword(s):  
Optimal Control ◽  
Linear Systems ◽  
Discrete Time ◽  
Control Method ◽  
Learning Method ◽  
Control Laws ◽  
Q Learning ◽  
Model Free ◽  
Quadratic Optimal Control ◽  
Time Linear

In this paper, the Q-learning method for quadratic optimal control problem of discrete-time linear systems is reconsidered. The theoretical results prove that the quadratic optimal controller cannot be solved directly due to the linear correlation of the data sets. The following corollaries have been made: (1) The correlation of data is the key factor in the success for the calculation of quadratic optimal control laws by Q-learning method; (2) The control laws for linear systems cannot be derived directly by the existing Q-learning method; (3) For nonlinear systems, there are some doubts about the data independence of current method. Therefore, it is necessary to discuss the probability of the controllers established by the existing Q-learning method. To solve this problem, based on the ridge regression, an improved model-free Q-learning quadratic optimal control method for discrete-time linear systems is proposed in this paper. Therefore, the computation process can be implemented correctly, and the effective controller can be solved. The simulation results show that the proposed method can not only overcome the problem caused by the data correlation, but also derive proper control laws for discrete-time linear systems.

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.

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