A Multi-agent Fuzzy-Reinforcement Learning Method for Continuous Domains

2005 ◽  
pp. 306-315 ◽  
Author(s):  
Erkan Duman ◽  
Mehmet Kaya ◽  
Erhan Akin
Keyword(s):  
Reinforcement Learning ◽  
Learning Method ◽  
Multi Agent ◽  
Continuous Domains

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.

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