DocumentCode :
3420327
Title :
Hierarchical modular reinforcement learning method and knowledge acquisition of state-action rule for multi-target problem
Author :
Ichimura, T. ; Igaue, Daisuke
Author_Institution :
Fac. of Manage. & Inf. Syst., Prefectural Univ. of Hiroshima, Hiroshima, Japan
fYear :
2013
fDate :
13-13 July 2013
Firstpage :
125
Lastpage :
130
Abstract :
Hierarchical Modular Reinforcement Learning (HMRL), consists of 2 layered learning where Profit Sharing works to plan a prey position in the higher layer and Q-learning method trains the state-actions to the target in the lower layer. In this paper, we expanded HMRL to multi-target problem to take the distance between targets to the consideration. The function, called `AT field´, can estimate the interests for an agent according to the distance between 2 agents and the advantage/disadvantage of the other agent. Moreover, the knowledge related to state-action rules is extracted by C4.5. The action under the situation is decided by using the acquired knowledge. To verify the effectiveness of proposed method, some experimental results are reported.
Keywords :
knowledge acquisition; learning (artificial intelligence); multi-agent systems; AT field function; HMRL; Q-learning method; agent distance; agent interest estimation; hierarchical modular reinforcement learning method; knowledge acquisition; multitarget problem; profit sharing; state-action rule; Computational modeling; Educational institutions; Knowledge acquisition; Learning (artificial intelligence); Reactive power; Safety; Simulation; C4.5 Knowledge Acquisition; Hierarchical Modular Reinforcement Learning; Multi-target; Profit Sharing; Q-learning; Reinforcement Learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence & Applications (IWCIA), 2013 IEEE Sixth International Workshop on
Conference_Location :
Hiroshima
ISSN :
1883-3977
Print_ISBN :
978-1-4673-5725-8
Type :
conf
DOI :
10.1109/IWCIA.2013.6624799
Filename :
6624799
Link To Document :
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