한국지능시스템학회:학술대회논문집 (Proceedings of the Korean Institute of Intelligent Systems Conference)
- 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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- Pages.66-69
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- 2003
Reinforcement Leaming Using a State Partition Method under Real Environment
- Saito, Ken (Graduate School of Engineering Tokyo Metropolitan institute of Technology) ;
- Masuda, Shiro (Graduate School of Engineering Tokyo Metropolitan institute of Technology) ;
- Yamaguchi, Toru (Graduate School of Engineering Tokyo Metropolitan institute of Technology)
- 발행 : 2003.09.01
초록
This paper considers a reinforcement learning(RL) which deals with real environments. Most reinforcement learning studies have been made by simulations because real-environment learning requires large computational cost and much time. Furthermore, it is more difficult to acquire many rewards efficiently in real environments than in virtual ones. The most important requirement to make real-environment learning successful is the appropriate construction of the state space. In this paper, to begin with, I show the basic overview of the reinforcement learning under real environments. Next, 1 introduce a state-space construction method under real environmental which is State Partition Method. Finally I apply this method to a robot navigation problem and compare it with conventional methods.
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