DocumentCode :
3597164
Title :
Cooperative Q-learning with heterogeneity in actions
Author :
Mirfattah, S. M Reza ; Ahmadabadi, Majid Nili
Author_Institution :
Robotics & AI Lab., Tehran Univ., Iran
Volume :
4
fYear :
2002
Abstract :
Cooperation in learning improves the speed of convergence and the quality of learning. Special care is needed when heterogeneous agents cooperate in learning. It is discussed that, cooperation in learning may cause the learning process to diverge if heterogeneity is not handled properly. In this paper, it is assumed that two heterogeneous Q-learning agents cooperate to learn. The heterogeneity is assumed in their action order (and not in their action set). A Q-learning-based method is introduced for the agents to learn the mapping among their actions. It is shown that, the agents are able to learn this mapping while cooperating in learning. Some simulation results are reported to show the effectiveness of the proposed method.
Keywords :
learning (artificial intelligence); multi-agent systems; action heterogeneity; action order; convergence; cooperative Q-learning; heterogeneous agent cooperation; multi-agent systems; simulation; Artificial intelligence; Convergence; Humans; Intelligent agent; Intelligent robots; Intelligent systems; Laboratories; Mathematics; Physics; Telecommunication control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 2002 IEEE International Conference on
ISSN :
1062-922X
Print_ISBN :
0-7803-7437-1
Type :
conf
DOI :
10.1109/ICSMC.2002.1173250
Filename :
1173250
Link To Document :
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