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Emergence of Information Processor Using Real World--Real-Time Learning of Pursuit Problem
Auckland, New Zealand December 13-December 15
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/HIS.2006.23Sixth International Conference on Hyb ...
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Hiroyuki Fujii, Okayama University, Japan
Kazuyuki Ito, Hosei University, Japan
Akio Gofuku, Okayama University, Japan
Real-time reinforcement learning is difficult because number of trials is too much to complete learning within limited time.

To solve the problem, we consider reduction of action-state space by information processor using real world without prior knowledge. We obtain the information processor in evolution by setting the fitness as ease of learning. As a typical example, we address pursuit problem in which dynamics is regarded. As a result, the processor has been obtained in evolution and agent has learned in real-time.

Citation:
Hiroyuki Fujii, Kazuyuki Ito, Akio Gofuku, "Emergence of Information Processor Using Real World--Real-Time Learning of Pursuit Problem," his, pp.7, Sixth International Conference on Hybrid Intelligent Systems (HIS'06), 2006
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