Online Support Vector Regression based Value Function Approximation for Reinforcement Learning

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This paper proposes the online Support Vector Regression (SVR) based value function approximation method for Reinforcement Learning (RL). This approach conserves the Support Vector Machine (SVM)'s good property, the generalization which is a key issue of function approximation. Online SVR can do incremental learning and automatically track variation of environment with time-varying characteristics. Using the online SVR, we can obtain the fast and good estimation of value function and achieve RL objective efficiently. Throughout simulation tests, the feasibility and usefulness of the proposed approach is demonstrated by comparison with SARSA and Q-learning.
Publisher
IEEE
Issue Date
2009-07-05
Language
ENG
Citation

IEEE International Symposium on Industrial Electronics, IEEE ISIE 2009, pp.449 - 454

URI
http://hdl.handle.net/10203/162242
Appears in Collection
EE-Conference Papers(학술회의논문)
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