대한전기학회:학술대회논문집 (Proceedings of the KIEE Conference)
- 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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- Pages.621-624
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- 2003
Acrobot Swing Up 제어를 위한 Credit-Assigned-CMAC 기반의 강화학습
Credit-Assigned-CMAC-based Reinforcement Learning with application to the Acrobot Swing Up Control Problem
초록
For real world applications of reinforcement learning techniques, function approximation or generalization will be required to avoid curse of dimensionality. For this, an improved function approximation-based reinforcement learning method is proposed to speed up convergence by using CA-CMAC(Credit-Assigned Cerebellar Model Articulation Controller). To show that our proposed CACRL(CA-CMAC-based Reinforcement Learning) performs better than the CRL(CMAC-based Reinforcement Learning), computer simulation results are illustrated, where a swing-up control problem of an acrobot is considered.