Q-learning algorithm in solving consensusability problem of discrete-time multi-agent systems

Automatica ◽  
2021 ◽  
Vol 128 ◽  
pp. 109576
Author(s):  
Tao Feng ◽  
Jilie Zhang ◽  
Yin Tong ◽  
Huaguang Zhang
Keyword(s):  
Discrete Time ◽  
Learning Algorithm ◽  
Multi Agent Systems ◽  
Q Learning ◽  
Agent Systems ◽  
Multi Agent

Improvement on Supporting Machine Learning Algorithm for Solving Problem in Immediate Decision Making

2012 ◽  
Vol 566 ◽  
pp. 572-579
Author(s):  
Abdolkarim Niazi ◽  
Norizah Redzuan ◽  
Raja Ishak Raja Hamzah ◽  
Sara Esfandiari
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Multi Agent Systems ◽  
Combined Model ◽  
Q Learning ◽  
Agent Systems ◽  
Multi Agent ◽  
Case Base ◽  
Case Base Reasoning ◽  
Robotic Tool

In this paper, a new algorithm based on case base reasoning and reinforcement learning (RL) is proposed to increase the convergence rate of the reinforcement learning algorithms. RL algorithms are very useful for solving wide variety decision problems when their models are not available and they must make decision correctly in every state of system, such as multi agent systems, artificial control systems, robotic, tool condition monitoring and etc. In the propose method, we investigate how making improved action selection in reinforcement learning (RL) algorithm. In the proposed method, the new combined model using case base reasoning systems and a new optimized function is proposed to select the action, which led to an increase in algorithms based on Q-learning. The algorithm mentioned was used for solving the problem of cooperative Markov’s games as one of the models of Markov based multi-agent systems. The results of experiments Indicated that the proposed algorithms perform better than the existing algorithms in terms of speed and accuracy of reaching the optimal policy.

scholarly journals Multiagent reinforcement learning using Non-Parametric Approximation

Respuestas ◽  
2018 ◽  
Vol 23 (2) ◽  
pp. 53-61
Author(s):  
David Luviano Cruz ◽  
Francesco José García Luna ◽  
Luis Asunción Pérez Domínguez
Keyword(s):  
Reinforcement Learning ◽  
Hybrid Control ◽  
Learning Algorithm ◽  
Multi Agent Systems ◽  
Generation Task ◽  
Q Learning ◽  
Agent Systems ◽  
Multi Agent ◽  
Optimal Set ◽  
Parametric Approximation

This paper presents a hybrid control proposal for multi-agent systems, where the advantages of the reinforcement learning and nonparametric functions are exploited. A modified version of the Q-learning algorithm is used which will provide data training for a Kernel, this approach will provide a sub optimal set of actions to be used by the agents. The proposed algorithm is experimentally tested in a path generation task in an unknown environment for mobile robots.

An iterative Q-learning based global consensus of discrete-time saturated multi-agent systems

2019 ◽  
Vol 29 (10) ◽  
pp. 103127 ◽  
Author(s):  
Mingkang Long ◽  
Housheng Su ◽  
Xiaoling Wang ◽  
Guo-Ping Jiang ◽  
Xiaofan Wang
Keyword(s):  
Discrete Time ◽  
Multi Agent Systems ◽  
Q Learning ◽  
Agent Systems ◽  
Global Consensus ◽  
Multi Agent

An Approach for Fault Tolerance in Multi-Agent Systems using Learning Agents

2015 ◽  
Vol 11 (3) ◽  
pp. 30-44
Author(s):  
Mounira Bouzahzah ◽  
Ramdane Maamri
Keyword(s):  
Fault Tolerance ◽  
Formal Model ◽  
Fault Tolerant ◽  
Learning Algorithm ◽  
Multi Agent Systems ◽  
Q Learning ◽  
Learning Agents ◽  
Agent Systems ◽  
Learning Agent ◽  
Multi Agent

Through this paper, the authors propose a new approach to get fault tolerant multi-agent systems using learning agents. Generally, the exceptions in the multi-agent system are divided into two main groups: private exceptions that are treated directly by the agents and global exceptions that combine all unexpected exceptions that need handlers to be solved. The proposed approach solves the problem of these global exceptions using learning agents. This work uses a formal model called hierarchical plans to model the activities of the system's agents in order to facilitate the exception detection and to model the communication with the learning agent. This latter uses a modified version of the Q Learning Algorithm in order to choose which handler can be used to solve an exceptions. The paper tries to give a new direction in the field of fault tolerance in multi-agent systems by using learning agents, the proposed solution makes it possible to adapt the handler used in case of failure within the context changes and treat repeated exceptions using learning agent experiences.

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